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To deeply analyze the performance of paper-cutting elements in interior space design, this paper explores the artistic expression of paper-cutting elements. The BIM model is constructed based on the TVL algorithm, and a noise-reducing style migration method with an additional total variable loss function is used to fuse the texture features of a specific paper-cutting style so that the paper-cutting texture is generated in the target object area more intensively. The algorithm is supplemented with a U-shaped network structure to obtain the mask map of the target object, modify and expand the ScanNet training data set, and input a content map and a style map to generate the stylized paper cutout image automatically. The results show that compared with other elements, the stability of paper-cut elements reaches 96.31%, the image smoothness reaches 89.34%, and the retention of significant regions reaches 93.26%. In modern design, the peak signal-noise of paper-cut elements improved by 13.418d on average, the structural similarity also improved by 0.34807, and the recognition rate of paper-cut elements in the furnishing category reached 95%. Applying paper-cut art in interior space design can give interior furnishing design better visual effects, provide inspiration and ideas for interior space design innovation, and contribute to improving aesthetics.

In the realm of Chinese traditional arts, paper-cutting symbols embody deep-seated aesthetic and cultural values. Within the framework of artificial intelligence, the fusion of these conventional symbols with contemporary pattern design through intelligent reconstruction processes promotes innovation in modern design. This paper introduces a novel pattern feature extraction algorithm specifically designed for identifying and extracting the essence of traditional paper-cutting art. Utilizing Deep Convolutional Generative Adversarial Networks (DCGAN), an automated method for generating woven patterns is developed, integrating conventional motifs with modern design elements. Additionally, a segmentation model based on DeepLabV3+ semantics is constructed to facilitate this integration. The study focuses on the fusion of traditional paper-cutting symbols with fabric patterns, providing a new direction for textile design research. The empirical evaluation involved seven experimenters who predominantly favored patterns that blended abstract and traditional styles, with the average style rating exceeding 3.2. Moreover, the color performance analysis of the reconstructed fabric patterns across five regions showed color difference values greater than 4, indicating superior color fidelity. These findings underscore the potential of combining AI-driven techniques with traditional art forms to enhance and revolutionize modern design practices.

The study of metadiscourse word resources is an important way to reveal academic knowledge constructs and genre features in English papers. This paper selects intra- and interdisciplinary English academic papers as data samples, and the text representation is based on the vector space model. The text similarity is calculated using the word movement distance model to complete the clustering based on the density peak, and then the semantic annotation of metadiscourse verb patterns is performed using the conditional random field. Then, the intra- and interdisciplinary metadiscourse annotations are compared to analyze the variation of verb patterns. The intra-disciplinary variation of metadiscourse verb patterns was mainly found using attitude markers, where the log-likelihood ratios of 48.56, 30.25, and 35.64 were obtained for affirmative, critical, and critical declarative attitudes, respectively. In interdisciplinary studies, basic disciplines preferred to use verb patterns as transitions, while applied disciplines preferred to express the author’s point of view. The study of metadiscourse verb patterns effectively enriches the linguistic resources in academic English teaching and deepens the understanding of the characteristics of disciplinary essay genres.

This investigation delves into the evolving patterns and regional variances in industry-academia collaboration (IAC) within Functional Nucleic Acids (FNAs) research. Analyzing publications and patents from 1990 to 2021, we dissected IAC at individual and collective levels, documenting the count and share of IAC endeavors, key partners, and regional differences. Results indicate a rising trend in IAC publications and collaborating institutions, while patent trends display a mix of increases, decreases, and steady states. The United States leads in IAC activities, correlating with its prowess in scientific research and technological innovation. Notably, regional discrepancies in IAC are mainly due to intra-regional variations, with Asia and Europe experiencing the most pronounced disparities.

Leveraging artificial intelligence (AI), this study revolutionizes examination management and education in universities by developing an intelligent system encompassing comprehensive management, pre-examination activities, scheduling, and preparation. The system also features a quality management component for educational outcomes. An enhanced genetic algorithm introduces an adaptation function to optimize intelligent grouping, facilitating effective exam paper distribution. Applied at Y University, our innovative approach significantly refines exam paper difficulty (ranging between 0.5016 and 0.5581) and differentiation (0.3845 to 0.4596), showcasing the intelligent algorithm’s effectiveness in exam management and contributing valuable insights to educational research.

Artificial intelligence (AI) and machine learning have become increasingly important in modern society and are poised to play an increasingly prominent role in education. This paper seeks to provide a theoretical framework for interrogating the integration of AI in education spaces. The paper argues that the eventual response of educators to recent developments in artificial intelligence is eerily like the earlier cycles of integrating ICT in education and, decades earlier, calculators into mathematics instruction. Premised on the argument that there are similarities between the calculator revolution in mathematics education and the ICT revolution in education several decades ago and the current ongoing developments in artificial intelligence, this paper offers a theoretical lens. The theoretical lens is composed of the Technology-Organization-Environment (TOE) framework, Technology Acceptance Model, Technological Pedagogical Content Knowledge, Socio-technical system theory, and Diffusion of Innovation theory. The paper concludes that despite spatial differences between the ICT revolution and the artificial intelligence revolution, there are shared similarities warranting adoption of a similar theoretical lens. Furthermore, factors that were considered pivotal in the integration of ICT are still relevant to the revolution of artificial intelligence. [ABSTRACT FROM AUTHOR]

In many digital systems, various sequential blocks are used. This paper is devoted to the case where the model of a Mealy finite state machine (FSM) represents the behaviour of a sequential block. The chip area occupied by an FSM circuit is one of the most important characteristics used in logic synthesis. In this paper, a method is proposed which aims at reducing LUT counts for FPGA-based Mealy FSMs with transformation of state codes into FSM outputs. This is done using the combined state codes. Such an approach allows excluding a block of transformation of binary state codes into extended state codes. The proposed method leads to LUT-based Mealy FSM circuits having exactly three levels of logic blocks. Under certain conditions, each function for any logic level is represented by a circuit including a single LUT. The proposed approach is illustrated with an example of synthesis. The results of experiments conducted using standard benchmarks show that the proposed method produces LUT-based FSM circuits with significantly smaller LUT counts than is the case for circuits produced by other investigated methods (Auto and One-hot of Vivado, JEDI, and transformation of binary codes into extended state codes). The LUT count is decreased by an average of 17.96 to 91.8%. Moreover, if some conditions are met, the decrease in the LUT count is accompanied with a slight improvement in the operating frequency compared with circuits based on extended state codes. The advantages of the proposed method multiply with increasing the numbers of FSM inputs and states.

A steady-state sinusoidal and distortion-free excitation source is very important for the accuracy and consistency of the calibration parameters of micro-electro-mechanical systems (MEMS) inertial sensors. To solve the problem that the current MEMS inertial measurement unit (IMU) calibration device is unable to reproduce the spatial motion of linear and angular vibration coupling, research topics on the coupling vibration characteristics and parameter identification for an electromagnetic linear-angular vibration exciter are proposed. This research paper used Ampere’s law and Lorentz force to establish the analytical expressions for the electromagnetic force and electromagnetic torque of the electromagnetic linear-angular vibration exciter. Then, the main purpose of this paper is to establish uniaxial and coupled vibration electromechanical analogy models containing mechanical parameters based on the admittance-type electromechanical analogy principle, and the parameter identification model is also obtained by combining the impedance formula with the additional mass method. Finally, the validity of the coupling vibration characteristics and the parameter identification model are verified by the frequency response simulation and the additional mass method, and the relative error of each parameter identification is within 5% in this paper.

With the deepening of higher vocational English teaching research and the continuous advancement of reform, this paper combines in-depth learning with higher vocational English teaching and makes an in-depth study of higher vocational English teaching practice based on in-depth learning theory and learning theory. First of all, the paper expounds on the concept of higher vocational English teaching and the research of English teaching. Explain the related concepts of psychological factors and the composition of psychological factors, then describe the model of in-depth learning, elaborate the related algorithms of in-depth learning optimization methods and the functions of various data of in-depth learning-related research, and finally, based on in-depth learning, explain the psychological factors of teachers and students in higher vocational English teaching. It reflects the importance of psychological factors in higher vocational English teaching. With the development of in-depth learning, we should apply in-depth learning to higher vocational English teaching and optimize the teaching mode of in-depth learning reasonably, including formulating comprehensive higher vocational English teaching objectives, adjusting students' psychological status reasonably, and carrying out cooperative learning.

The cultural hedging and fusion of the new era embodied in literary works is of great significance to the communication, development, and prosperity of Chinese and Western cultures. This paper selects the English works compiled and published by Lin Yutang as the research object and analyzes the communication value generated in the process of its compilation and publication. After designing a questionnaire qualified by reliability and relevance analysis, the questionnaire is used to investigate the audience’s exposure to English works and the degree of understanding of the newage cultural hedging embodied in the works. The results of the survey show that multimedia has become the main channel for audiences to access Lin Yutang’s English works. The audience’s understanding of the new age cultural hedges embodied in the works is poor; the acceptance of the new age cultural hedges is at a medium level (3.514 points), and there is a significant correlation between the acceptance level and the understanding effect. It is also found that the multimedia communication method consisting of video and text in a compiled publication has the best comprehension effect (4.857 points) on the new age cultural hedging of the audience. This paper provides a reference path for the compilation and publication of Lin Yutang’s English works under multimedia and lays a foundation for deepening the audience’s understanding of the new age cultural hedge.

With the rapid development of the information age, new technological means are constantly applied to the field of education and produce good results. This paper uses data mining algorithms to extract and analyze data on college English education and explore how to adjust the teaching methodology. On the basis of improving the inherent defects of the DPC algorithm, this paper determines the clustering center through a scoring function, uses the K-neighbor method to form the cluster backbone, and proposes a nearest-neighbor-based label propagation method to propagate the labels to their adjacent nodes to complete data mining. After mining and analyzing the current situation of English education, it was found that the age group over 30 years old accounted for 77.6%, female teachers accounted for 74.6% of the total number of teachers, teachers with the titles of lecturer and assistant professor accounted for 58%, and 33% of the students were not too satisfied or dissatisfied with the content of the English course. It shows that the English teaching team of universities in this province has shortcomings such as gender imbalance, insufficient young and senior teachers, and less than satisfactory content of English courses, which need to be improved in several aspects.

Migration learning is a kind of deep learning that plays an important role in risk prediction and warning. In this paper, we use the Triplet-loss representation learning technique to map data samples of the same category to adjacent spatial regions, map data samples of different categories to different spatial regions, and add domain adaptive computation to complete domain adaptation. XGBoost performance is also optimized to improve the prediction results. After completing the risk prediction model construction, this paper also incorporates indicator weights to propose a risk warning model based on a weighted knowledge graph. The analysis found that the model accuracy of the compliance risk prediction model is 1.155% higher than that of two traditional prediction models on average, and the indicators of accuracy, F1-score, and AUC are all over 0.99, which indicates that it has high accuracy. After applying the optimized risk management system in an enterprise, it is found that the weight of the indicators of the enterprise’s compliance management system reaches 25.71%, the combined weight of the legal audit system and the risk prevention and control mechanism is 16.23% and 14.10%, respectively, and the combined weight of the two indicators of the appraisal and evaluation of the performance of the compliance management and the accountability for the violation of the law exceeds 8%. The weights of the indicators related to the degree of business standardization are close to 16.51%, indicating that the enterprise’s daily production and operation activities are standardized and orderly, and the perfection of the compliance management system is in line with the real situation of the enterprise. It can be seen that the risk management system makes accurate judgments on the compliance risk of the selected enterprise, which reflects the feasibility of the application of the new model.

This paper builds a business English intelligent teaching network platform through cloud computing, virtual reality, corpus and automatic scoring system, and other information technologies. Educational information technology helps construct a linguistic corpus by utilizing vocabulary lookup and MI values to create a one-stop solution for multi-level language analysis and application. The essay scoring project in the Business English Smart Teaching Network Platform utilizes the bag-of-words vector model to extract the features of each word, text, and topic to complete automatic essay scoring. After analysis, it can be seen that the business skills and interests of the experimental group have significantly improved after the teaching of the Business English Wisdom Teaching Network Platform. After the corpus-scaffolded writing teaching training, the mean values of students’ vocabulary richness ranged from 3.869 to 4.108, and sentence variety ranged from 3.961 to 4.135. Automatic scoring of compositions resulted in a significant improvement in the writing quality of students (P=0.000). The smart teaching network platform mentioned in this paper can be utilized to improve business English teaching in the context of informationization.

Nautical talents are the first resource and fundamental guarantee for the development of marine careers, strengthening the construction of nautical talents and realizing the effective supply of nautical talents have become the urgent task of building a strong marine country. This paper combines the basic situation of the current nautical personnel training and the training requirements of nautical personnel, constructs the vocational literacy and ability enhancement path of nautical students based on the integration of post-course, race, and certificate under the guidance of the OBE concept, and constructs the corresponding index system. Subsequently, combining the hierarchical analysis method and entropy weight method, the weights of the indicators in the evaluation system of nautical students’ vocational quality and talent ability are calculated, and the cloud model is utilized to evaluate the vocational quality and talent ability of nautical talents after cultivation. The results of the study show that the comprehensive evaluation level of the vocational quality and talent ability of the maritime students of Jimei University after the implementation of the pathway is “good”. Among the secondary evaluation indexes, the vocational concept evaluation cloud has the highest degree of affiliation, which is 9.31. This indicates that the vocational concept of navigation students is being cultivated at a very high level. The evaluation result of “vocational behavior” also belongs to the “good” level. The cultivation path proposed in this paper provides a reference for the enhancement of the vocational quality and ability of nautical talents, and it can improve the cultivation of nautical talents through the synergistic approach of government, industry, enterprise, and school, and guarantee the enhancement of the vocational ability and career expansion of nautical talents.

Recent years have seen intensive research and widespread application of 6G wireless transmission technology, providing a technical bridge for the application of speech network analysis to spoken English teaching. Thus, this paper suggests a method for improving 6G technology. This paper primarily focuses on the following areas: First, to prevent overfitting, the dropout technique under 6G is added to the model, and the speech feature extracted from the data by the coding network is directly utilized. We then utilize the feature for either reconstruction or classification purposes. Initially, we perform unsupervised reconstruction using unlabeled data, and once we obtain the network parameters, we use the labeled data for classification. The next step involves feeding both data simultaneously into the model to calculate the error. Finally, we use gradient descent to minimize the error and optimize the parameters until the model converges. This model can convert sound-related symbols or information into visual symbols, and it can also convert speech into text. Comparative analysis shows that the improved method in this paper is far better than other methods in terms of noise reduction, and the signal-to-noise ratio of this paper is 3.76 dB higher than that of the LMS algorithm and 1.36 dB higher than that of the spectral subtraction method, which can stimulate the students’ interest in learning spoken English and increase the student’s interest in learning by 13.64% as a whole. It can be summarized through the comparison that this paper’s improved method increases the clarity of speech network analysis, which helps students teach spoken English.

In this paper, the convolutional neural network is applied to the processing of stylistic features in the images of landscape paintings of Wei Jin and North and South Dynasties. After convolution and pooling activation, rich and less parametric feature maps are generated to lay a solid foundation for generative adversarial networks. Meanwhile, in order to ensure the rigor of the image dataset of landscape painting images under scene transformation, an improvement is proposed on the basis of traditional GAN technology, and a function represents the improved CycleGAN model. The experimental dataset has been constructed, and the loss function, network training, and hyperparameters have been designed for the model. The visual and conversion outcomes of the model-generated images are evaluated through simulation experiments. The migration of all landscape painting style textures is within the range of (0.5,2.4) at a gray level of 8. The migration styles increase as the gray level increases, and the data between groups does not overlap. In the subjective evaluation of the landscape painting style transformation generated by the model used in this paper, the score of the generated image is 3.8135 points, while the number of high scores is more than 65% of the votes. The model constructed in this paper has a better effect on the transformation of the image style of the Wei, Jin, and North and South Dynasties, and it can satisfy people’s visual needs.

This paper takes green finance policy as the research object and collects local government policy texts about green finance so as to study its policy structure in depth. Then, it studies the theory of green finance to support the low-carbon economy, empirically analyzes it in terms of measurement using the panel vector autoregressive model, and tests its smoothness and covariance to explore the impact of green finance in the development of the low-carbon economy. According to the findings, the proportion of demand-based green financial policies is the lowest among the five provinces, with a mean value of 22%. The extreme differences in development levels of a low-carbon economy and green finance are 1.52 and 1.46, respectively, showing different characteristics. The model regression coefficient of the level of green financial development is 0.08, and the development of a low-carbon economy positively influences it. Although the effectiveness of green financial policies will fluctuate, the overall trend is upward. This paper analyzes the panel vector autoregressive model and the effectiveness of policy tools to provide suggestions for improving the policy system.

In the face of people’s growing spiritual civilization needs, the application of traditional art colors in modern visual communication design has become a hot spot of attention. In this paper, a model for extracting traditional art color features is constructed to provide a methodology and theory for applying traditional art color in modern visual communication design. In the model construction, the max-min clustering algorithm is proposed to achieve color segmentation, measure the distance between the clustering center and the samples, and improve computing speed. Based on the K-means algorithm, the initial clustering center selection and the number of class clusters are improved, and the color feature extraction method is optimized to improve the accuracy of color extraction. In the performance experiments of the model, the All, Noncc, and Disc parameters of this paper’s color segmentation algorithm are lower than those of other algorithms in color segmentation of different pictures, and the performance is good. The accuracy of the color feature extraction test in different color spaces is close to 100%, the average verification accuracy is as high as 99.8%, and it takes less time. The aesthetic, originality, symbolism, and spirituality evaluation indexes of the poster work “Lead,” designed by combining the model of this paper, have an average score of more than 4, which reaches a good level.

How to carry out scientific and reasonable teaching design and then improve the teaching effect is a problem faced by many college English teachers. Aiming at such problems, this paper is based on the six elements of the BOPPPS teaching model to design a blended teaching mode of university English and finally designs a blended teaching mode of university English based on the BOPPPS teaching model. In order to confirm the practicality of the teaching model, research subjects and research materials are identified, and after collecting sample data through questionnaires, the independent sample t-test algorithm is used to analyze the effect of the teaching model designed in this paper. The data show that there is a significant difference between the experimental group and the control group in the five qualities of scientific thinking, with a p-value of less than 0.05, and in terms of English achievement, the corresponding t-test result is a sig (two-sided) of 0.007

In the context of energy saving and emission reduction to protect the environment has become a social consensus, environmental benefit is a social responsibility that enterprises must actively undertake, and the technological advantages of information technology companies make them more equipped to enhance environmental benefits. Therefore, this paper proposes countermeasures for information technology companies to enhance their social responsibility and environmental benefits. To achieve practical countermeasures for energy savings and emission reduction, this paper proposes a method to improve the LSTM power prediction model using the Sparrow search algorithm for the company’s electricity management. The model optimizes the network structure of traditional LSTM by searching for parameters such as learning rate, iteration number, and the number of neurons in the two hidden layers of LSTM through the sparrow fitness function. The model proposed in this paper has an average prediction error rate of 3.32% in predicting the annual electricity consumption of a county for five years from 2018 to 2022. After an enterprise introduced the social responsibility practice countermeasures proposed in this paper, the company’s net profit margin increased by 12.2% on average, CO2 emission reduction was 963.2 tons, the average monthly electricity consumption was reduced by 24936.5 kWh, and the environmental performance score increased from 3.2 to 5.4.

In the context of practical education, deepening the reform of quality education and implementing the fundamental task of cultivating moral integrity have become the guidelines and the basis for the development of curriculum reform in colleges and universities. This study conducts data mining based on the association rule model on the influencing factors of the reform of the Civic and Political Theory Course in colleges and universities to obtain the key influencing factors in the process of the reform of the theory course. Then, under the perspective of deep learning, the idea of practical parenting is integrated, and the structural framework of Civic and Political Theory course reform is designed from four directions: learning theme, learning objectives, learning activities, and evaluation feedback, and finally, the course reform method of this paper is examined through teaching practice. The results show that the influence weights of self-factors, school education factors, family factors, and social factors are 0.3022, 0.3521, 0.1381, and 0.2076, respectively, with self-factors and school education as the key influencing factors. After the teaching practice, the P-value of each dimension of the students under the curriculum reform based on this paper is less than 0.05, and the grade of the Civics and Political Science Theory course is improved from 81.47 to 90.45, which is an increase of 7.49 points compared with the class of the regular course. The reform method based on deep learning in this study can promote students’ development and improve their academic performance, which provides a reference for the research on the reform and development of the Civics and Political Science Theory course.

This paper proposes a reform of the teaching methodology based on the exercise prescription course centered on intelligent exercise prescription recommendation and designs a specific implementation plan. A criterion function is used to determine and represent the constant terms of the user-set data using the K-means clustering algorithm. The personal data of users is added on this basis, and the attribute weighting is used to calculate user similarity and construct the user matrix. The core parameters of intelligentized exercise prescription are examined, and the relevant data of the exerciser group is obtained through modeling. The set of similar cases is determined, and the exercise effects in the set of similar cases are fused and calculated, and the effect level of the fused effects in the cases is discriminated. The model developed in this paper is implemented in the college physical education classroom, and the student’s attitudes toward physical exercise and fitness levels are analyzed. The results show that the attitude towards physical exercise behavior of students in group A is significantly better than that of those in group B. The significance test (F = 7.985, Sig = 0.005) is less than 0.05. Taking the 50-meter sprint as an example, the difference in the duration between male students and female students in groups A and B is 0.83s and 0.9s, respectively, which is a large enhancement, which shows that the intelligent exercise prescription has a positive impact on physical education teaching.

Ethnic music culture is a rare and precious artistic treasure in all national cultures, and the inheritance and promotion of ethnic music culture is an essential part of music education teaching in colleges and universities. This paper delves into the characteristics of the development and dissemination of ethnic music culture in the Internet era, with the aim of studying the integration of ethnic music culture into music education teaching in colleges and universities through the use of Internet technology. Through the collaborative filtering algorithm, students’ preferences for ethnic music works are mined so as to recommend accurate teaching resources for college students on the ethnic music online teaching platform constructed based on the collaborative filtering algorithm. In order to fully prove the effectiveness of the teaching platform designed in this paper, the rough set theory and neural network algorithm are used to construct the online teaching quality evaluation model for college ethnic music. The questionnaire survey method is used to study the status quo of ethnic music teaching in colleges and universities, and it is concluded that at present, there are problems in ethnic music teaching in colleges and universities, such as insufficient concepts of students learning ethnic music and insufficient attention to ethnic music in colleges and universities themselves. Through the analysis of the effect of the recommended algorithm, the design of the evaluation index system, and the practical application, the quantitative assessment value of the teaching quality of the ethnic music online teaching platform designed in this paper in the practical application is 92.11, which indicates that the comprehensive rating of the quality of the ethnic music online teaching in colleges and universities in this paper is excellent.

Cultivating innovative talents is an important goal of the talent cultivation strategy in the new era, and ideological and political education is an indispensable part of cultivating a correct outlook on life and the spirit of innovation. In order to fully leverage the supporting role of ideological and political education in cultivating innovative talents, this paper proposes an enhanced collaborative filtering algorithm and content recommendation algorithm. The traditional Civic and Political learning evaluation algorithm is improved by considering the forgetting factor and introducing disciplinary abilities and sequence dependence. The results of the experimental analysis point out that the average grade improvement of the seven Civics and Politics knowledge points of the students in the experimental class reaches 10.3%, the mean value of the four innovation dimensions reaches 14.95 points, and the average agreement rate between the self-assessment results of the learning effect and the results of the evaluation model is 90%. It shows that the optimized Civics personalized learning and evaluation method, based on this paper, has positive effects.

In recent years, with the interaction and integration of modern cultural and creative industries with Chinese excellent traditional culture, various innovative cultural and creative products have been introduced, ushering in new opportunities for the inheritance and development of Chinese excellent traditional culture. In order to integrate Chinese cultural elements into the design of cultural and creative products. This paper draws on the characteristics of DNA inheritance and mutation in living organisms to construct a multi-objective optimization model of DNA elements, and by applying the model to the analysis stage of cultural and creative product design, the designed products can be more completely integrated into the elements of Chinese characteristic culture to effectively guide the design practice of cultural and creative products. This paper takes Dunhuang Tang Dynasty Feitian cultural elements as an example for practical research through the integration of Dunhuang Tang Dynasty Feitian cultural elements of a series of cultural and creative products for the satisfaction survey from the symbolic perception, beautiful shape, functional and practical, cultural connotations, innovation and novelty of the five dimensions of the design effect assessment, the furniture category of cultural and creative products scored the highest, the average value of 4.766, the cultural and creative products in the symbolic perception of the dimensions of the score is high. It shows that in the Dunhuang Tang Dynasty, Feitian cultural and creative products can be perceived as symbols of the characteristic culture of the period.

The rapid development of the digital economy makes the center of gravity of enterprise development accelerate from the consumer field to the production and manufacturing field, penetrating all aspects of the daily operation and management of an enterprise. This paper presents relevant management strategies by exploring the impact of the ecosystem model of enterprise management innovation on the impact of enterprise management in the digital context. In order to measure the comprehensive efficiency value, the efficiency value of capital absorption, and the efficiency value of investment after enterprise management, the theory of antagonistic cross-efficiency is introduced, the antagonistic cross-DEA model is constructed, and the evaluation steps of enterprise management effect are given. Using this model to empirically analyze the management effectiveness of China’s construction industry, enterprise performance=4.563+0.352*digital innovation ecosystem index+0.236* periodic cost+0.362*number of R&D personnel, and the digital innovation ecosystem is significantly related to enterprise performance. Finally, this paper designs a corporate management strategy for the digital innovation ecosystem in the context of the digital economy from three perspectives.

The trend of our country into an aging society is becoming more and more obvious, and the elderly education service has become the focus of social attention. The integration of artificial intelligence into elderly education is one of the inspiring ideas in which the personalized recommendation algorithm can recommend educational resources according to the characteristics of older people, and it has the prospect of application. In this paper, we first provide a CTransD-GAT recommendation model based on a knowledge graph, which improves traditional problems such as data sparsity by setting weight preference and feature aggregation. A dynamic preference-capturing method is proposed based on contextual interaction to capture the variable user learning interests more accurately and flexibly. This paper examines the practical utility of personalized recommendation methods for educational resources based on these two improved techniques. The post-test mean score of each knowledge module test of the experimental group is improved by 1.83 points compared with the pre-test, 11.46 points improve the score of teaching ability, and the scores of perceived usefulness, ease of use, and intention to use are 3.82, 3.89, and 3.97, respectively. It shows that the improved educational resource recommendation model has an excellent effect on improving knowledge structure and teaching ability, and it is characterized by simplicity and ease of use.

This paper takes three colleges and universities in a province as an example, designs relevant questionnaires and distributes them, and analyzes the collected data by using statistical methods such as independent samples t-test and ANOVA one-way ANOVA. The paper analyzes the data collected using statistical methods such as independent sample T-tests and ANOVA to infer the overall situation of the current level of emotional identification with Marxist theory among Chinese college students. The data was examined in detail to determine the variations in the emotional recognition of Marxist theory among college students based on their gender, ethnicity, and professional classification. The results show that more than 50% of college students understand Marxism and strongly identify with it. The overall mean score of affective identification is 35.324, which is a good status for identification. In terms of different genders, the P-values of value identity, knowledge identity, social identity, and behavioral identity are less than 0.1, and there are significant differences. Emotional identity does not have significant differences in different ethnicities (P = 0.382 > 0.05) and different majors (P = 0.426 > 0.05).

Based on digital media technology, this paper defines Chinese art theory and, at the same time, combines digital media technology and the characteristics of Chinese art aesthetic discourse to construct an art aesthetic discourse system that covers the stage of sensory experience and the stage of “emotional experience”. In order to promote the digitalization of the art aesthetic discourse system, the MCFF algorithm is used to extract the features of art aesthetic discourse, match the features, and then merge the features of art aesthetic discourse based on the weighted summing algorithm so as to achieve the digitization of art aesthetic discourse features. The art aesthetic discourse system in digital media is analyzed based on the research data. The results show that compared with the traditional algorithm, the algorithm of this paper is better than the traditional algorithm in three aspects: pre-emphasis (growth rate of 62.70%), frame-plus-window (growth rate of 68.71%), and data conversion (growth rate of 72.14%). In addition, the aesthetic experiences of landscape painting and figure painting were 96.49±8.4 and 108.00±9.35 points, respectively, and F(1, 20)=20.746, p

In recent years, the number of psychological problems occurring in college students has been increasing day by day, and timely, convenient, and accurate psychological warning is an important way to prevent college students from psychological crises. Starting from the basic needs of the system and aiming at realizing the embedding of data mining technology in the psychological management system, this paper elaborates on the design and implementation of the data mining technology module suitable for the psychological management system. The clustering algorithm-K mean algorithm of data mining is used to distinguish groups of students with different categories of psychological problems, which provides data support for the next decision-making. Through simulation experiments between the system designed in this paper and four other mental health management systems, the clustering performance and system evaluation performance of this paper’s system are better than those of the other systems. The result of mining the mental health of college students through this system shows that 82.2% of the student’s assessment results show normal, and 8.35% of the students need to focus on, and this result is not much different from that obtained from the statistical survey of a personality questionnaire, which indicates that the use of clustering analysis in the system can be an effective prediction of the mental health of college students.

Carbon emission from building construction is an important issue in the construction industry, and the continuous application of management and assessment techniques forms a new trend of carbon emission reduction. This paper establishes a multi-objective optimization model for building construction carbon emission based on multidimensional data analysis of four objectives: schedule, cost, quality, and carbon emission. The particle swarm algorithm is improved by dynamically adjusting the inertia weight factor and penalty function to deal with constraints and is used to solve the multi-objective optimization model. Building construction cases are selected to predict and analyze the combinations of optimal execution modes under different teams, to compare the optimization results of configuration schemes and the efficiency of algorithms, and to further propose the practical path of carbon emission reduction in building construction. The study addresses the construction combination mode that meets the needs of various decision-makers, and the team 3 combination mode has the lowest carbon emission (228.41kg). The multi-objective optimization scheme under carbon emission constraints optimizes between 3.93% and 21.78% in the four objective dimensions. This paper further expands the scope of the multi-objective optimization model for building construction focusing on the “low carbon” objective, improves the feasibility of the configuration scheme, and then promotes the green and stable development of the construction industry.

Along with the continuous development of multimedia technology and the deepening of teaching information, virtual reality design factors are crucial to enhancing the impact of the teaching experience. The purpose of this paper is to address traditional art education’s transformational needs and develop an art education teaching system that utilizes virtual reality technology. It also uses the eye-tracking instrument to collect student eye movement data, and after preprocessing the data, such as missing data and noisy trajectory points, it counts the results of each eye movement index in the teaching process. The study showed that some of the students’ eye movement indicators during teaching had a strong correlation with the level of cognitive engagement in learning (P 0.05) when the design elements of the teaching interface were paired with the top of the bottom of the picture and the middle size and the top of the picture and the bottom of the picture and the large size. This paper provides references and their significance for related research, as well as the design of a virtual simulation platform for art teaching.

The quality of higher vocational nautical English teaching is not only related to the student’s vocational ability and job competence but even related to the safety of the ship and personal safety during the voyage. In this paper, we design a reform plan for higher vocational nautical English teaching based on the Hadoop-MapReduce distributed system framework, design the reading and writing algorithms of teaching resources, use a genetic algorithm to improve Pick-KX dynamic load balancing algorithm and construct a nautical English project-based teaching resource base. The effectiveness of the improved Pick-KX dynamic load balancing algorithm in this paper is tested through experiments with 200 to 1000 concurrent users. Finally, two senior nautical English classes are selected to conduct a semester of controlled variable experiments on project-based teaching reform and analyze the changes in performance. It was found that when the number of online users increased from 200 to 1,000, the IO and memory occupancy increased by 60% and 458M, respectively, and the average time taken increased from 0.8 to 5.8 seconds, which was in a reasonable range, and the repository was able to run smoothly. After one semester of experiment, the average score of the experimental class increased by 10.88 points, and the distribution of scores changed towards the high end of the range, while the scores of the control class did not change significantly. This paper’s nautical English project-based teaching resource library is effective, and the project-based teaching reform is a significant improvement in students’ nautical English scores. This study provides unique ideas and feasible methods for the reform of nautical English teaching, as well as the application and innovation of project-based teaching.

In the context of big data, traditional detection algorithms can no longer meet today’s needs. For traditional sensors, there are shortcomings such as long working times, high complexity, and high false detection rates. This paper proposes a new type of sensor and an algorithm for detecting anomalies in sensors based on Flink. The sensor network senses the monitoring targets in the monitoring area in real time and transmits them to the end users through wireless communication. Then, the fusion rule for target detection in sensor networks is studied. Then, the detection algorithm is computed using Flink and predicted using a sliding window and ARIMA model on the Flink platform. Then, the confidence intervals of the prediction results are calculated, and the outliers are evaluated. Finally, the K-Means++ algorithm is used for clustering and probability assessment of previously obtained outliers. Then, compared with traditional sensors, comparative analysis shows that the proposed method in this paper has some advantages in terms of both sensitivity (S) and quality factor (Q). The value of S is more than 320 than the 2D PhC sensor array. The value of Q is more than 5295 than the 2D PhC sensor array. This paper’s new sensor detection method is more diverse than the traditional method. The detection time is reduced. The detection accuracy is also improved compared to the traditional method. It can be summarized by comparison that the new sensor in this paper is more excellent than the traditional method of detection.

The explosive development of artificial intelligence technology greatly promotes the intelligent construction of museums and strengthens their cultural inheritance role. This paper takes artificial intelligence technology as its guide, analyzes the advantages of AI technology in the intelligent museum, and establishes the specific structure of an intelligent museum by combining VR technology. Wisdom Museum can realize the effective repair and presentation of cultural relics data. This paper uses three-dimensional laser scanning technology to obtain cultural relics point cloud data, repair the holes in the point cloud data through RBF, optimize the point cloud data by combining texture mapping, and input the optimized data into the GC-GAN model to realize the three-dimensional digital repair of cultural relics images. Moreover, the smart museum’s intelligent interaction system is built by combining virtual reality technology and related equipment. Taking Dunhuang mural point cloud data as an example, the number of point clouds of the RBF hole repair algorithm differs from the original data by only 5.86%, and the SSIM and PSNR values on the repaired portraits are 0.83 and 26.36 dB, respectively. The SUS total score for the user on the intelligent interactive system is 73.875, and the UEQ factor scores are averaged out at 1-3 points. The creation of a smart museum can be realized using AI technology, providing users with an immersive cultural relics viewing experience and activating the vitality of all kinds of cultural relics in the museum.

Enhancing the mainstream ideological identity of young people is an unavoidable issue in the ideological construction of socialism with Chinese characteristics in the new era, which is closely related to strengthening the national identity and expanding the mainstream ideological positions. In this paper, we refer to the E-learning platform framework to design a Marxism online learning platform to achieve the online dissemination of Marxism and ideological education for youth. The HRNN model is utilized to extract and identify the behavioral traits of youth on the Marxism learning platform and investigate the link between youth behavior and ideological identity. The results of the study show that the Marxist online learning platform has a better communication effect and can improve the identification of the youth group with Marxist ideology. Meanwhile, the correlation analysis shows that there is a significant correlation between video viewing completion, quiz completion, and ideological identity (P=0.000), which is highly positive. The suggestions put forward in this paper regarding communication management, communication technology, and publicity provide a reference for further online communication of Marxism and lay the foundation for the enhancement of youth ideology education.

In modern society, innovation and entrepreneurship education have gradually transcended the traditional paradigm, intertwined with the concept of commonwealth, and constructed a new picture of vitality and social sentiment. In terms of research ideas, this paper extracts value-shaping elements, service learning elements, social creation faculty elements, social creation curriculum elements, organizational leadership elements, interest group elements, and social creation practical elements as the elements of entrepreneurial competence cultivation of college students according to their growth pattern, the entrepreneurial environment they have, and entrepreneurial resources they possess. We obtained the four crucial factors for individual, group, school, and social levels through micro-level analysis. We constructed the model for cultivating entrepreneurial competence in college students with the support of seven elements and four hierarchical factors. In the end, the cultivation model was analyzed using the PLS-SEM model. It has been verified that 11 out of the 13 hypotheses proposed in this paper were supported through testing. 11 cultivation paths that passed the significance test were found after further analysis. The most significant path is the SF->IE->PO path under the policy anchoring force, with effect sizes and t-statistics of 0.325 and 7.494, respectively. In this way, we have refined the methodology of cultivating co-enrichment and entrepreneurship skills among university students.

As a classic representative of traditional culture, Chinese calligraphy art has more and more obvious advantages in today’s digital media art creation. In this paper, the calligraphic text image is binarized by the OTSU method, the image enhancement is realized by linear gray value stretching, and the optimal tilt correction method is used to preprocess the traditional calligraphic text image and make a font library. Based on the data from the calligraphy font library, an immersive digital interactive context of traditional calligraphy is designed using digital media art. Pix2Pix network is introduced to combine with the PatchGAN network to establish a style migration network for traditional calligraphy text images, and three different types of loss functions are designed to ensure the accurate generation of traditional calligraphy text. The L1 loss of this paper’s model in generating traditional calligraphy characters is only 0.0359 at the lowest, the similarity values of the generated samples of calligraphy characters are mainly between 90% and 96%, and the maximum error of the relative position and relative size of the radicals is only 1.18%. The average value of the G2R indicator for the users’ images of calligraphic characters was 75.23%, and 89.86% of the users indicated that they were basically satisfied with the immersive interactive context of traditional calligraphy. Traditional calligraphy can achieve digital transformation and provide audiences with more diverse cultural experiences by relying on digital media art.

The guerrilla warfare behind enemy lines of the national government is still a weak link in the study of the history of the war of resistance, and the naval guerrilla warfare on the Yangtze River is even less specifically discussed. This paper first analyzes the change in the national government’s naval operational thinking after the July 7 Incident and up to the Battle of Wuhan and divides the naval operations in the Yangtze River Basin during the period of total war of resistance into four stages. Then, combined with the ontology theory and taking various archives, museums, libraries, and memorial halls as sources, after fusing heterogeneous data, it adopts the relevant writings, notes, diaries, speeches, photographs, objects of the Navy’s Yangtze River Resistance as well as the results of the archive compilation formed later to construct the Navy’s Yangtze River Resistance Archives Knowledge Graph Model. After the reasoning and searching of the knowledge map, it is concluded that in 1938 and 1940, the Yangtze River Bray Guerrilla Group killed 950 and 3020 enemies, respectively, destroyed 20,400 tons and 118,400 tons of warships, strongly supported the Battle of Wuhan and two Battles of Xiangbei, and prevented the Japanese army from invading to the west along the river, and in 1940, it destroyed the Japanese large-scale transports and merchant ships of a total of 58,700 tons, which dealt a heavy blow to the Japanese army on the Yangtze River and the Yangtze River. Heavy blows to the Japanese water transport capacity of the Yangtze River delayed its front and rear troops, weapons, supplies, and transportation plans so that its ability to expand received a major limitation, unable to continue the attack on the Yangtze River west of Wuhan, to defend the safety of Chongqing and Sichuan. This paper makes up for the research gaps in the history of the National Government’s naval resistance and the history of guerrilla battles in the Bray of the Yangtze River Basin and provides a useful exploration of the combination of resistance history research and information technology.

In the long historical evolution of humanity, religion has always demonstrated social, and cultural patterns and historical forms and has silently influenced some people’s ideological concepts and moral behaviors at all stages of development. This paper uses deep learning to investigate how the popularization of religious culture affects the dissemination of the concept of racial justice. The LBP-ORB algorithm model is constructed for the popularization method of religious culture, which is based on the improvement of the ORB algorithm, and the cognitive degree of different religious cultures can be improved by feature extraction through the LBP-ORB algorithm model. The model of this paper will be compared experimentally and then through fieldwork. The LBP-ORB algorithm model will be used to extract the features of local religious culture and popularize the local youths. Based on the analysis results, we explore the effect of the popularization of the cognition of religious culture on the spread of the concept of racial justice. After the popularization of religious culture, the college student group and middle school student group’s knowledge of the concept of racial justice increased substantially, with the number of people familiar with the college student group increasing by 88 and the number of people with general knowledge increasing by 122. The number of people in the middle school student population who were familiar with it reached 112, and the number of people with a general understanding reached 92. This paper’s LBPORB algorithm model has significantly increased the dissemination of the concept of racial justice after popularizing religious culture.

The ADDIE model is highly compatible with teaching in terms of educational purposes, concepts, and processes and has a positive contribution to teaching. In this study, the ADDIE model is used to teach a comprehensive practice course for automobile majors in order to promote the systematic design and implementation of teaching. In this paper, the main problems to be solved are firstly extracted through the elemental analysis of the comprehensive practice course system of automobile majors, and the expected level of learning objectives to be achieved is pre-set. Then, a complete integration of educational media resources is carried out to test the effectiveness of the developed course instructional design. The fuzzy evaluation model is utilized to assess the quality of the course constructed by the ADDIE model. Specifically, the hierarchical analysis method is applied to calculate the index weights, and then the entropy weight method is used to correct the obtained index weights to obtain the final weights, determine the fuzzy operator, and make a comprehensive assessment of the teaching quality. It can be seen through the analysis that the method proposed in this paper makes the average final grade of the experimental class higher than that of the control class by 3.4524 points, and the final rating of teaching quality obtained by the method of this paper using the fuzzy comprehensive evaluation model is 3.7968 points, which is a good level. This indicates that the upgraded method of teaching practical courses is more effective than the original method, enhances the overall performance of students, and fulfills the requirements for teaching practice in automotive specialties.

Under the era of intelligence and informational technology, society has set higher requirements for the cultivation of talents in colleges and universities. This paper builds up a school-enterprise cooperation training model on the cultivation demand of applied talents in finance and taxation, and the employment quality data after the application of the model is statistically analyzed with the data of H universities in Guangdong Province. At the same time, after constructing the evaluation index system of the quality of the training of applied talents in finance and taxation under the model of school-enterprise cooperation by using the hierarchical analysis method and assigning weights to it, the quality of the training of the talents is comprehensively evaluated by the fuzzy comprehensive evaluation method. It was found that the employment rate of H college increased from 74.17% to 97.59% in the past ten years, and the counterpart rate increased from 73.54% to 89.24% in 2014. The study shows that A10 Employer Enterprise Satisfaction, A2 Enterprise Cooperation Platform, and A6 Enterprise Mentor Guidance have the highest scores of 86.215, 84.296, and 84.137, respectively, indicating that enterprises play an important and positive role. The A3 curriculum and A9 employment quality scores are below 80, which suggests that there are still problems to be resolved in both the course and employment quality. This paper provides useful exploration to address the problem of inadequate and misplaced school-enterprise integration and to ensure a comprehensive improvement in the quality of training talent in finance and taxation.

In order to promote the development of the Chinese gardening field, this paper proposes a data-driven, multi-sensory design for Chinese gardens. The multisensory design of Chinese gardens, which combines data-driven technology and VR technology, is explored from two perspectives: the visual and auditory senses. It is found that the design easily leads to the loss of the natural beauty of the garden. For this reason, the layout optimization model is constructed using the improved PSO algorithm, and the corresponding analysis method is applied to analyze the effect of the multi-sensory design of the garden. The results show that the model in this paper is better than the other two control models in terms of weighted mean (4.078) and convergence time (0–4 ms). It was also found that the basis for visitors to make experience visual satisfaction evaluations was basically whether the floral landscape was beautiful (39.18%) and whether the field of view was open (23.27%), and the auditory experience design evaluation scores were higher for acacia forests (5.436), red-leafed ridges (4.091), snapdragon forests (3.872), and metasequoia forests (3.657) in the gardens. The research in this paper reveals the focus of multi-sensory design in Chinese gardens, which can provide theoretical references for research in the field of Chinese garden design.

Education informatization is an important initiative for the country to realize education modernization, which is related to the development of China’s education. The use of a single evaluation of teaching effectiveness obviously cannot meet the assessment needs. Based on the premise of intelligent assistance through a hybrid brain-computer interface, this paper aims to study an evaluation method for teaching effectiveness using artificial intelligence. Firstly, EEG signals are collected through an EEG acquisition device, and then they are preprocessed. Second, the pre-processed signals are subjected to feature extraction, and then the features are classified using a specific pattern recognition method, and finally the classification results are used to assess the effectiveness of English teaching for college students. Based on the evaluation results, the learning system is optimized and improved. The signals collected by EEG devices carry a lot of noise, and in this paper, the processing of the original signals includes a smoothing filter, de-baseline filter, filter filter, and Kalman filter to achieve a higher signal-to-noise ratio. Multivariate evaluation results are provided by the evaluation system that is assisted by artificial intelligence. Compared with other evaluation methods to enrich the evaluation information, based on the above system effectiveness evaluation study, as far as the F1 value is concerned, this paper’s method is the highest, reaching 0.815, which is 0.048 higher than the highest F1 value of the traditional algorithm, and in the other two values, this paper’s method is also higher than other methods. This verifies the rationality and effectiveness of the teaching effectiveness evaluation system based on a hybrid brain-computer interface proposed in this paper.

Although AI technology has been widely utilized in many fields and has reaped good reviews. However, there is a vacancy in the education field for AI technology to achieve personalized recommendations for the English language. In this paper, based on the traditional cognitive diagnostic model, we propose optimizing two aspects, namely question type and mastery state. Combining the neighborhood recommendation algorithm, the English learning recommendation model is constructed using fuzzy cognitive diagnosis. Based on the theory of adaptive learning, we have designed a personalized bank of recommended English learning questions. The model is utilized in the real world of English learning, and a control experiment is designed to evaluate the student’s knowledge mastery and the impact of the model application. The experiment shows that student A has a better mastery of S1 and S3, with mastery levels of 0.856 and 0.815, respectively, but only 0.235 for S4, and needs to customize a more targeted personalized learning plan and strategy for S4-related content. The average scores of D1 and D2 before practice were 73.51 and 72.18, and after practicing through the personalized recommending English learning methods proposed in this paper, the English score of the D2 class was improved to 85.33, and the t-test result between the two groups was significant p-value of 0.002, which is less than 0.01, indicating that there is a significant difference between the two groups and that the model proposed in this paper has a significant enhancement on English learning.

Under the requirements of the educational background of the new era, establishing moral character has become the fundamental task of education, and at the same time, it also brings new challenges and opportunities for English education in higher vocational colleges and universities. Based on the core qualities of the curriculum Civics and Politics, we explore the point of fit between English education and it so as to deeply analyze the integration mechanism of the curriculum Civics and Politics in English education and complete the construction of the teaching model of the curriculum Civics and Politics in English education by combining with the big data technology. On this basis, taking S higher vocational colleges applying this model as an example, the expert scoring method is used to construct the teaching quality evaluation index system and assign weights, and the fuzzy comprehensive evaluation model is used to assess the quality of education. Core literacy and comprehensive ability have the highest weights of 0.2836 and 0.2866 respectively, respectively, which are key areas for cultivation. Finally, through the quality evaluation of the education model of this paper, it can be seen that the highest score of comprehensive ability is 90.21, which is at the excellent level, and the lowest score of values cultivation is 81.98. and the scores of each index are above 80. The average score of the education model of this paper is 85.13, which proves that the quality of teaching under the teaching model is good. It has an excellent performance in the cultivation of English talents. It is a good example of Big Data Technology, and the integration of the English education program Civics provides a feasibility with practical significance.

Under the advancement of reform and opening-up policy, the development direction of e-commerce enterprises has gradually shifted from the city to the rural areas with lower levels of economic development. This paper builds a rural e-commerce intelligent logistics system based on the Internet of Things (IoT) technology and proposes an improved ant colony algorithm to plan the optimal path of rural e-commerce logistics and distribution by mining the transfer probability of each distribution point in logistics and distribution as well as pheromone updating mechanism, so as to improve the logistics efficiency and customer satisfaction. Experiments show that the improved ACO algorithm in this paper performs significantly better than the genetic algorithm and particle swarm algorithm in logistics and distribution path planning and can control the transportation cost and penalty cost at a lower level. When considering the cargo loss of fresh products in logistics distribution, the satisfaction rate of traditional logistics distribution is only 65.2%, while the satisfaction rate of logistics distribution based on intelligent logistics technology is 87.9% on average. The distribution path planned by the ant colony algorithm has less mileage, lower cost, and higher logistics efficiency. This paper provides a feasible path for further improvement of rural e-commerce logistics efficiency, which can promote the development of the logistics industry in the direction of more intelligent, personalized, and green.

Agricultural operators can predict the yield of wheat at different stages of growth, development, and harvesting and take different measures to realize precise management. The purpose of this paper is to apply agricultural mechanical engineering automation to wheat yield prediction, and a UAV multimodal data wheat yield prediction model is developed using the RMGF algorithm. Different data sources, such as vertical distribution of terrain and spatial variability, canopy height and wheat plant height, canopy temperature difference, vegetation spectral characteristics, and vegetation index, were extracted using an agricultural UAV. Then GF decomposition algorithm based on MSD decomposes the multimodal image into an approximate image and detail image, and after optimization of the fused weight map using RSA, the fused image is obtained by IMST according to the optimized weight map. The model was used to carry out regression analysis of yield prediction for three types of wheat, heat-tolerant, medium heat-tolerant, and high-temperature-sensitive, and finally predicted the wheat yield from 2015 to 2024 in a production area. It was found that the R² of the RMGF multimodal model in this paper predicted the three kinds of wheat yields as 0.7936, 0.8609, and 0.9262 with excellent accuracy results. The predicted yields were basically in line with the actual yields in the high-yield portion, with large prediction errors above 9000 kg/ha. The prediction error for wheat was within 0-2.26%, and the predicted yield in a main wheat production area was 7050 kg/ha in 2024. This study provides a feasible method for large-scale yield estimation in the main production area, which contributes to high-throughput plant phenotyping and agricultural precision reform.

Music education, as an important part of art education, can also take advantage of artificial intelligence technology to achieve more efficient personalized teaching. The direction of the application of artificial intelligence technology in music course learning is explored in this paper. Therefore, a music data technology interactive learning system is proposed. The music recognition module’s design utilizes a deep neural network model to model the complex problem of speech signal recognition. The encoder formula is obtained by representing the implicit layer feature vectors in the sample through a mathematical model. After encoding and decoding, as well as designing the activation function, the HMM algorithm is introduced to realize the application of DNN-HMM in acoustic modeling. Using a digital filter, the spectrum of the speech signal is smoothed, and the spectrogram is obtained by Fourier variation to visualize the representation of the speech frequency domain. The design of a music Internet teaching course is based on the method proposed in this paper. The melody recognition accuracy of the system is tested through simulation experiments, in which the distribution of auditory feature points of the piano ranges from 0.66 to 0.69. The distribution of rock music is above 0.7, and there is no overlap between the two audio datasets, which indicates that the system proposed in this paper has good recognition accuracy of audio features. Using the speech analysis module, the students’ music learning performance is analyzed. After the model designed in this paper to assist music learning, students’ music performance mean value is 4.397, and the control group’s performance is 3.565. The difference is 0.832. The system designed in this paper is more effective for music learning.

In the era of smart tourism, its emergence not only provides crucial technical support to meet the needs of tourists but also prompts the reform and improvement of the development mode of the tourism and culture industries. This study begins by examining the current state of smart tourism and the development of the tourism and culture industries. It then conducts a thorough analysis of the mechanisms driving these developments and establishes an industry analysis model based on the RMP theory. The study then employs fractional regression to identify the factors that influence the development of the smart tourism and culture industry. It then constructs a model for smart tourism and culture development, which includes government and industry support, economic and cultural support, digital information technology support, personnel support, talent cultivation, cultural participation, and social participation. Finally, it analyzes the impact of this model’s application using the RMP analysis model. This paper’s development model sees annual increases in tourism and culture industry indexes, rising from 105.85 to 145.79, with an average growth ratio of 17.35%. The overall satisfaction rating for the product experience stands at 4.152, suggesting that under the development mode of the smart tourism and culture industry in this paper, both the market economy and the product experience exhibit excellent phenotypes. This paper supports the construction of a smart tourism cultural industry development model using digital technology and provides references for the tourism cultural industry model.