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With the rapid development of Internet, new media news has gradually become the most concerned information source of new media people. New media public opinion is a force that cannot be ignored. It needs monitoring and guidance. The research on hot topic discovery and trend analysis can timely find social hot topics and analyze the trend of topics, which is conducive to grasp the trend of public opinion, so as to correctly guide and maintain social stability. At the same time, the emerging industry of new media has sprung up like mushrooms after a rain, with a rapid momentum. With the advent of the era of big data, the development of new media presents characteristics and advantages that are different from traditional media, but we should also note that the era of big data has both advantages and disadvantages for the development of new media. In this paper, LDA and ARIMA models can be used to calculate and analyze the popularity measurement and trend analysis of new media reports under the background of big data mining. The model designed in this paper has a conclusion: the missed detection rate is reduced by 75.4%. Through experiments, it is found that the accuracy of heat topic detection of the model designed in this paper can reach 84.6%. In trend analysis, the first stage of transmission is called the incubation period. Then after a certain critical point, it will come to the outbreak period. The outbreak period lasted for a period of time, entered a period of plateau, and finally came to a subsidence period. [ABSTRACT FROM AUTHOR]

An interpersonal relationship is a relationship that arises and develops in the process of people's extensive material and spiritual exchanges. College students, as a special group, not only have the general characteristics of modern young people's interpersonal communication but also have remarkable characteristics of their own communication. The purpose of this paper is to study how to use intelligent big data to conduct a brief survey and research on the interpersonal relationships of college students so that college students can discover their own problems and establish sound and good interpersonal relationships. This paper proposes data mining based on intelligent big data. Data mining plays an important role in information processing. Data mining includes many algorithms. This paper selects cluster analysis for the need to classify the influencing factors in interpersonal relationships. The cluster analysis algorithm can classify similar information well. The experimental results of this paper show that the most important skills given by the five companies that are considered to be necessary for talents are the interpersonal relationship, self-ability, and academic qualifications. Among them, five companies gave the highest score of 8.9 points to the interpersonal relationship and the lowest score of 8.0 points, with an average of about 8 points, while the scores of their own ability and education were below 8 points. It can be seen that entrepreneurs think that the necessary skills for talents are not academic qualifications and their own abilities but good interpersonal relationships. Therefore, it is very important to establish good interpersonal relationships. [ABSTRACT FROM AUTHOR]

With the acceleration of economic globalization, the demand for Japanese professionals is also increasing. In view of this situation, this paper puts forward a set of college Japanese test systems. A college Japanese teaching model based on big data is established, and an improved clustering algorithm is proposed by comparing correlation analysis and data mining techniques. Aiming at the phenomenon that ACA (ant colony algorithm) cannot guarantee that all data objects are acquired and collected by ants and the same data object can be accessed repeatedly, this paper puts forward some corresponding improvements. The clustering results show that the clustering accuracy of the algorithm is 80%. This proves the rationality and feasibility of dynamically evaluating students' achievements based on clustering algorithm, and it is of great significance to introduce data mining into the teaching environment. [ABSTRACT FROM AUTHOR]

In the big data environment, the factors affecting the operation of small- and medium-sized enterprises are becoming more and more complex. In order to more accurately measure the financial crisis of small- and medium-sized enterprises, from the perspective of multisource information fusion, based on the traditional financial data reflecting the private information of enterprises, the public information data for measuring the macroeconomic and market environment are integrated and analyzed. Considering the multidimensional heterogeneity of multisource information data, BPNN, SVM, KNN, LOG, and MDA are introduced to build models, and ensemble learning is used to integrate the results of different early warning models to reduce the risk of inconsistent results from different models. The empirical results show that the early warning model integrating multisource information can improve the early warning accuracy. The research results show that the financial crisis early warning of small- and medium-sized enterprises should not only pay attention to financial indicators but also pay attention to the impact of corporate governance, industry, and the overall economic environment of the country; the accuracy of the model in this paper is significantly higher than that of the other models in terms of the accuracy of financial crisis early warning. It shows that the model in this paper can better predict the real financial status of small- and medium-sized enterprises. [ABSTRACT FROM AUTHOR]

In recent years, the network can gradually express more and more complex systems. In the process of network expression, the basic elements of the system are defined as nodes, and the interaction relationship is defined as links. In the current application process, network data are diversified, some express feature structure information, and some express node attribute data. In practice, there is a lot of feature information of network nodes, many of which are redundant. How to filter out the redundant and useless feature information has become an important problem. So that, the feature network is applied, and the node feature correction method is designed. The weight method is used to express the relative importance of the feature problem. At present, the main discovery algorithms can only find the network structure features, and the clustering algorithm can only find the feature information about the network nodes. Therefore, this paper builds an embedded platform based on the remote control and constructs an embedded motion monitoring system. This paper starts with experimental research and completes the design of nutrition scheme through big data analysis method to enhance students' physique. In this paper, we use the feature network to complete the embedded sports monitoring system and put forward several suggestions to improve sports nutrition management. [ABSTRACT FROM AUTHOR]

At present, the irrational allocation of IPE (ideological and political education) resources in universities reflects the dilemma of university's control of university students' IPE system. In order to make educational big data really useful to us and really promote the sharing of educational resources and improve the utilization rate of resources, this paper discusses the construction of data resource sharing platform for college students in IPE field based on DL (Deep learning) based on the characteristics and existing problems of educational big data. Based on DL, the collected data are parameterized, the matching algorithm parameters are determined, the dynamic data sample subset is incrementally reduced, the discernibility matrix and logical analytic formula are established, the conjunctive normal form is obtained through calculation, and the kernel attribute is introduced into each conjunctions to realize incremental mining of dynamic data. The results show that the model in this paper achieves the best classification effect on six samples, with an average classification accuracy of 88.81%. The results show that the comprehensive shared data matching algorithm based on DL can realize the matching in a short time, and the matching process has high stability. [ABSTRACT FROM AUTHOR]

In order to strengthen the supervision and management of environmental pollution and timely understand and record the basic information of potential environmental pollution of enterprises and institutions, this paper proposes a general survey and inventory method of pollution sources based on big data technology under machine learning. Firstly, this paper evaluates and screens the data provided by government departments, constructs a machine learning classification model, and uses a variety of classification algorithms to compare and analyze according to the basic idea of machine learning to deal with practical problems. Then, the calibration data set constructed is used as the training set to predict and classify the national industrial and commercial data, provincial industrial and commercial data, and municipal industrial and commercial data provided by government departments. The experimental results show that the naive Bayesian classification algorithm is the best algorithm, and the F1 values of each data set are increased by 32.92%, 21.42%, and 14.91%, respectively. The classified prediction of the screened Internet data shows that the accuracy of the final Internet supplementary data is 17.26%, which is similar to the industrial and commercial data of the city. The availability of the machine learning model established in this paper is proven. [ABSTRACT FROM AUTHOR]

With the continuous growth of the film market and the consumption demand of audiences, the value of film content has become increasingly apparent. Extracting movie content elements is an important step in quantitative analysis of movie content. In this paper, based on text mining technology, deep data analysis of movie reviews is carried out using TF-IDF and machine learning to visualize high-frequency words, and sentiment analysis is performed on reviews to find out the hidden deep information behind the big data of movie reviews. Although the extracted keywords can draw the content and characteristics of the film for us, they cannot establish the correlation with the creative elements of the film and television works. Therefore, in this paper, the extracted keywords are clustered to find the central words of these words. First of all, in order to improve the representativeness of keywords, the Epoch data set and the Batchsize data set are used to conduct experimental analysis on the model in this paper. Through comparative experiments, it is concluded that when Batchsize = 32 and Epoch = 25, the model achieves the optimal classification effect. The analysis shows that when the training times are too small, the model is not fully learned, and when the training times are too many, the model will be overfitted, which will reduce the accuracy of the model, resulting in the opposite effect. The keywords extracted above can draw the content and characteristics of the movie for us. [ABSTRACT FROM AUTHOR]

In recent years, the development of intelligent iteration technology and the use of big data processing technology have set off an upsurge, and the analysis and application of artificial intelligence algorithms have been paid more and more attention. In order to face the challenges of Arab migration and international trade, this paper constructs the basic structure of the Arab migration action imitation model. In this paper, a simulated servo clustering algorithm based on big data and intelligent iteration is used. Then, through the analysis of pseudo servo clustering algorithm, an optimization model is established, and a big data analysis system is formed. This paper focuses on the wide application of big data statistics to solve the construction of Arab immigration and entrepreneurship data system. This paper studies and applies the big data statistics and intelligent iterative algorithm of Arab immigration behavior, focuses on the annual ladder degree of Arab immigrants, and constructs a pseudo servo cluster system based on Intelligent iterative algorithm. Finally, the simulation experiment verifies whether the clustering model can accurately retrieve the behavior of Arab immigrants in China. The era of big data provides good development opportunities for Arab immigrants and international trade, but it also faces severe challenges. The study provides a reference for strengthening the analysis of international trade, and puts forward perfect countermeasures in combination with the actual situation, so as to improve the efficiency of international trade management and promote the better implementation of international trade. [ABSTRACT FROM AUTHOR]

Since sustainable development has become the dominant mode of human development at the present stage, green technology has received more and more attention under this background. The development of green technology has become an important means to achieve sustainable development. Green technological innovation is a kind of technological innovation. However, because the goal of green technological innovation is different from that of traditional technological innovation, the dynamic mechanism of green technological innovation lies in the similarities and differences of traditional technological innovation. This paper focuses on data mining technology to design and optimize the enterprise green technology creation model. At present, clustering algorithm and association rule algorithm are important research contents in big data mining technology. Among them, the clustering algorithm refers to the process of grouping similar data objects in a large amount of data information, so that the approximate data information can be aggregated and clustered, which is convenient for data mining calculation. In the algorithm, the shortcomings of the original clustering algorithm, such as insufficient data processing and incomplete analysis, are improved, and the data processing is improved by 51.7%, which has a good processing effect on subsequent data preprocessing and dynamic incremental clustering. In the follow-up experiment, the role of green technology in corporate finance is reflected. [ABSTRACT FROM AUTHOR]

The demand for energy in factories is huge. With abundant energy supply, factories can raise their production capacity to a new height. Energy is the material basis of domestic economic development. As the largest developing country in the world, energy shortage has become an urgent problem for China. In this paper, an Internet of Things based on big data ecosystem is proposed to analyze the energy consumption of the factory and build a model. The Internet of Things technology of big data ecosystem can be summarized as a technology that uses information sensing devices to complete the transaction and network connection according to the protocol content. A total of 853,000 power distribution operations were carried out in the power grid. In 2019, the average ratio of decision tree algorithm, machine learning algorithm, and machine learning algorithm was 36.8%, 37.4%, and 43.5%, respectively. Compared with the three methods, the method in this paper increased by 37.9% year-on-year and reduced the power outage by 2.63 million households, which is equivalent to a corresponding reduction of 35 users per operation. The functional requirements of the IoT energy consumption analysis system in factories based on big data ecosystem are reflected in three aspects: energy consumption monitoring and management, power control and management, and energy consumption supervision and analysis. Based on the management of energy consumption monitoring and power control through the software platform, the functional requirements of the system are analyzed. [ABSTRACT FROM AUTHOR]

The utilization and accessibility of big data and digital media technology by common people have decreased technological prices. Professional education has been pushed to modernize curricula and boost talent development efficiency. The vocational education curriculum system must take into account the usage of new technologies in a comprehensive manner to ensure the availability of knowledge and ability structure. To accomplish the goal, the application techniques and approaches of teaching and practice utilizing big data and digital media technologies in vocational education are explored and researched. To improve the teaching quality of higher vocational education, with the help of big data, this paper aims to conduct an in-depth study on the application of digital media technology in higher vocational education. Firstly, the teaching management system of higher vocational education is constructed. Secondly, the structure, efficient units, architecture, and database of the teaching management system are pronounced. Thirdly, fuzziness mining and teaching data scheduling are used to optimize and regulate the storage structure. According to the multiparameter optimization method, the teaching feature screening and resource scheduling are completed improving the capability of parameter recognition and online identification related to teaching resources. The simulation results show that the method proposed in this paper can make the teaching methods of higher vocational education more diverse. It is anticipated that it can help the reform process and growth of vocational education, as well as the development of innovative and entrepreneurial skills. As the data receiving rate of teaching resources is high, therefore, the ability of teaching resource scheduling is also strong along with a high application value. [ABSTRACT FROM AUTHOR]

In order to fully tap the potential of tennis players, speed, strength, and endurance are further improved in physical factors. Improve the overall competitiveness of tennis to a higher level and further improve the scientific level of tennis training. This paper truly reflects the adaptability of athletes' functional state to training load. At the same time, the data mining algorithm is used to analyze the correlation between athletes and athletes in the application of techniques and tactics. The results show that timely adjustment of training plan and training load can provide a scientific and objective basis for improving the guidance of combat readiness training. At the same time, adjusting the training plan and training load provides a scientific and objective basis for further improving the guidance of combat readiness training. This paper improves the metacognitive level of athletes' participation, accurately and timely adjusts the athletes' personal goals and realistic positioning, and timely feeds back the relevant information of the competition. Only by being good at creating a competition environment can athletes give full play to their advantages and actively seek and pursue improvement in the stage of competitive ability. [ABSTRACT FROM AUTHOR]

As the most important strategic resource of enterprises, big data has become the basic background of business model innovation. From the perspective of psychological contract, this paper discusses the mechanism of psychological contract in customer value proposition driven business model innovation and puts forward four research hypotheses. This paper adopts the confirmatory factor analysis method of structural equation to verify these four hypotheses. It is concluded that there is a significant positive relationship between customer value proposition and psychological contract; there is a significant positive correlation between psychological contract and business model innovation; and psychological contract has intermediary effect between value proposition and business model innovation. Furthermore, value proposition has a significant positive correlation with business model innovation, which has not been verified. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet information technology, the amount of various types of data has surged, and people have entered the era of Big Data. With the changes in the market environment, media environment and consumer behaviors, the marketing method in the mobile Internet era is bound to be an integrated, interactive, and precise marketing method that connects online and offline. In the context of this era, luxury brand marketing improves brand competitiveness by combining new marketing methods. The method used in this paper is machine learning. Many studies have shown that the larger the sample size processed by machine learning, the more accurate the effect of machine learning. Therefore, machine learning will become the core force in Big Data technology. Among them, this paper adopts the decision tree model, which first classifies the data, then uses the induction principle to generate readable decision tree rules and finally analyses and judges the decision data. According to the test results, the arithmetic mean calculation method is used to calculate the operational efficiency of the data flow balance height. The algorithm proposed in this paper is 20% more effective than the traditional algorithm. [ABSTRACT FROM AUTHOR]

Political and ideological online teaching is a new thing integrating political and ideological theory teaching and artificial intelligence information technology. In order to effectively improve the quality of political and ideological online teaching, this paper aims to make an in-depth study on the application of artificial intelligence technology in the political and ideological online classroom system under the background of big data. This paper first expounds the overall architecture of the political and ideological online system from the system hardware architecture and software architecture and then discusses in detail the functional design of the online system and the structure of the database. This paper proposes a data mining algorithm based on scale affine transformation and curriculum data information space reconstruction, constructs the data structure model of the political and ideological online classroom system, uses scale affine transformation to fuse the curriculum data, carries out spatial reconstruction of the fused curriculum data, extracts the high-order features of the curriculum data in the political and ideological online classroom system in the reconstructed space, and completes the data mining of political and ideological courses. Experiments show that the political and ideological online classroom system under the proposed method is better than the political and ideological online classroom system under the traditional method in terms of user satisfaction and teacher operation proficiency and can effectively improve the quality of political and ideological online teaching. [ABSTRACT FROM AUTHOR]

With the continuous development of information technology, China has officially entered into the era of big data, the enterprise business management has realized intelligent business management based on information technology, and the arrival of the era of big data has to some extent reduced its management and risk costs and improved its work efficiency and economic benefits, but at the same time, it also faces a series of challenges, such as with the continuous expansion of business scope. As the business scope continues to expand, its current management model cannot meet the development needs. The integration of artificial intelligence technology and enterprise business management has become the main trend of future development. The application of big data technology to corporate business management enables the production of products and the creation of product value in the direction of socialization, increases the mutual influence between companies and consumers, and enhances the fairness, openness, and justice between corporate development and consumer activities. In this paper, after a detailed understanding of the current situation of the combination of artificial intelligence technology and enterprise business management, we focus on the application path of artificial intelligence technology in enterprise business management and finally elaborate the future development direction of artificial intelligence technology in the field of enterprise business management, hoping to further deepen the relevant personnel's understanding of artificial intelligence. Analyzing the current situation of business management development in China, this paper proposes applying big data technology and artificial intelligence technology to enterprise business management work and proposes a new model to bring new development opportunities and development directions for enterprise business management work. The big data technology proposed in this paper is based on cloud platform services, while artificial intelligence based on neural network is embedded in the cloud platform for enterprise business management services. [ABSTRACT FROM AUTHOR]

In the era of big data, the value of data is infinite. With the development of the Internet of Things, everything is interconnected, and the sharing and circulation of data are particularly important. This paper was aimed at studying the data sharing system based on blockchain and big data technology. Based on blockchain and big data technology, this paper proposes a data-sharing system based on an HDFS file system. And it strengthens the security of data sharing based on blockchain technology, proposes a security key for industrial data, and greatly upgrades the security of data sharing. Experiments in this paper have proved that the data-sharing system in this paper has strong robustness, and choosing the appropriate k and n can take into account both the computational overhead and security, such as (3, 5), (5, 10), and (6, 10). [ABSTRACT FROM AUTHOR]

The establishment of English and French corpora has provided great convenience for language application and research. However, in the field of Japanese language research, the construction of Japanese corpus has been slow due to the limitation of Japanese markup format. This paper discusses the scientificity, rationality, feasibility, and construction of "microcorpus" and "specialized corpus" for the application of Japanese corpus in Japanese language teaching. Textual corpus is a corpus that contains all text but no media such as audio and pictures, and it is mostly used in teaching. The Japanese language corpus is a corpus created by analyzing the existing Japanese classroom teaching situation in response to the current reform of Japanese language teaching mode. It is important for teaching Japanese in colleges and universities because it can help students to learn independently and improve their learning initiative and motivation. The teaching-based text corpus is a small corpus for Japanese language teaching and learning, which is derived from the teaching process. The corpus can be created independently on an ordinary PC machine through software, and the collected corpus can be imported into the corpus and used, which provides another way to improve students' interest and learning ability on their own. Based on the search software, this paper selects representative novels from the Aozora Library and creates a simple corpus to serve the teaching of Japanese interpretation, which improves the teaching effect. At the same time, this paper discusses the boundaries and difficulties of individual self-built corpora with a view to making breakthroughs and progress in future research and development. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of artificial intelligence technology and big data application and the continuous optimization of algorithms such as deep learning algorithms and reinforcement learning algorithms, tourist attractions using "smart tourism" technology can obtain more accurate and in-depth knowledge from tourism big data and mine the value behind the data. As an important branch of tourism, the rise of rural tourism has inspired rural areas to protect the local environment, inherit characteristic culture, develop tourism resources, develop tourism economy, and increase rural income, which has well responded to the call of the rural revitalization strategy. However, due to lack of information resources and technology, some rural scenic spots still adopt traditional marketing methods without the support of big data and artificial intelligence technology. This paper proposes a research work on the marketing strategy of rural tourist attractions based on big data and artificial intelligence, analyzes current application of big data and artificial intelligence in rural tourism, and discusses the transformation of rural tourism marketing to a new fusion model with the support of big data and artificial intelligence, aiming to study every step involved and supported deeply by big data and artificial intelligence, from the awakening of intentions before the travel to the analysis of behaviors and preferences during the travel and the collection of evaluation after the travel. The research results of this paper show that more sufficient and deep integration between rural tourism and artificial intelligence/big data are needed to make tourists get better tour experience and marketing strategy of rural scenic spots to get better effects and income. [ABSTRACT FROM AUTHOR]

To solve the problem that the explosive growth of data cannot be effectively analyzed, a big data analysis and prediction system based on deep learning is proposed. The systems proposed in this paper optimize the design of circulatory neural networks using an optimization processing strategy in addition to circulatory neural networks based on a distributed parallel processing framework that studies and analyzes disrupted neural networks during the in-depth study. The results are as follows: using the DCNNPS parallel strategy can effectively improve the training speed of the convolutional neural network to a small extent, and the speed can be improved by more than 50%. Compared with the other two, the network optimized by DCNNPS is less sensitive to data growth in the scenario of a large amount of data and is more suitable for processing a large amount of data. Moreover, with the increase of computing nodes, the acceleration effect is more obvious, the acceleration ratio is higher, and the improvement energy is about 70%. It is proved that the system proposed in this paper can process massive data very effectively and make a great contribution to the development of the industry. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet, the information on the Internet presents an explosive growth. Cloud computing and big data analysis technology based on Internet information rise accordingly. However, all web pages contain not only important information but also the noise information irrelevant to the subject information. They seriously affect the accuracy of information extraction, so the research of web page information extraction technology arises at the historic moment and becomes the research hotspot. The quality of web page text information will directly affect the accuracy of later information processing and decision-making. If we can accurately evaluate the information of the web pages captured from the Internet and classify the extracted web pages according to the corresponding characteristics, we can not only improve the efficiency of information processing, but also improve the practical value of the information decision-making system. From the practical application requirements and user-friendly operation point of view, the information visualization of web design based on big data is studied in this paper. Specifically, the system designed in this paper improves the traditional template-based web information extraction method, establishes a web information extraction rule scheme combined with templates, and achieves the goal of web information extraction rule selection and template generation in the visual environment. Finally, the visualization algorithm based on T-SNE verifies the effectiveness of the web page information visualization algorithm designed in this paper. [ABSTRACT FROM AUTHOR]

There have been few studies on the quality assessment at the meso- or microlevel in my country's colleges and universities despite the fact that innovation and entrepreneurship (IAE) education is constantly progressing. A new assessment method based on big data is employed in this paper to investigate the design and management of microteaching modes for IAE education and to promote the development of IAE at these institutions while also enhancing the quality of innovative talent training. This paper addresses the urgent need to reform the evaluation system for IAE education. This paper has completed the following work: (1) this paper firstly analyzes the current situation of IAE education evaluation at home and abroad on the basis of literature research and summarizes the current theoretical thinking, methods, and shortcomings of IAE education evaluation. (2) Analyzing the present condition of IAE education assessment, relevant ANN technologies are introduced, and a suitable quality index system for IAE education is created. (3) With the use of big data, we were able to gather samples and data sets for the experiment, and then, we utilized the BP algorithm and the GA-BP algorithm to compare their accuracy in predicting the quality of IAE education, and the GA-BP algorithm has better performance. [ABSTRACT FROM AUTHOR]

With the development of contemporary technology and education, there are more and more innovative teaching methods that are helpful to the education and teaching procedure. Interactive teaching method is one of them. Interactive teaching is a teaching modus based on developed on the scaffolding teaching theory that attaches importance to the communication between teachers and learners. Its biggest goal is to develop learner-specific and specific autonomous learning strategies through the communication and collaboration between the teacher and the learner. The advanced nature and effectiveness of interactive teaching methods make it more and more widely used in education, such as in the field of music teaching. Music teaching is a kind of teaching that pays more attention to the communication between teachers and Teachees. This paper aims to study the interactive music teaching method based on BD and CC, that is to design an interactive music teaching method based on big data and cloud computing technology, and conduct practical application experiments on it. The experiment concluded that the interactive music teaching method based on BD and CC has made the students' participation rate in music teaching classrooms reach 88%, and the excellent rate of in-class test scores has reached 90%. [ABSTRACT FROM AUTHOR]

The so-called multimodal information refers to the information from different information sources on different or the same side of the same description target. These pieces of information are different in terms of storage structure, representation, semantic connotation, credibility, and emphasis, but there is a certain inevitable connection between them. This paper aims to study how to analyze and study the low carbon of enterprises with the help of multimodal information fusion based on the background of big data and construct the evolutionary neural network of the improved adaptive genetic algorithm. This paper puts forward the problem of low carbon in enterprises, which is based on multimodal information fusion, and then elaborates on the concept and related algorithms of multimodal information fusion. Information fusion has carried out case design and analysis. Through the research and analysis of enterprise low carbon and self-adaptive algorithm, it can be obtained that the neural network has reached the threshold of 3.95 after iterating for nearly 60 generations, and stopped iterating to obtain the best individual. The evolutionary neural network in this paper reaches stability after a small number of iterations and can basically achieve a certain low carbonization. [ABSTRACT FROM AUTHOR]

As the most important strategic resource of enterprises, big data has become the basic background of business model innovation. From the perspective of psychological contract, this paper discusses the mechanism of psychological contract in customer value proposition driven business model innovation and puts forward four research hypotheses. This paper adopts the confirmatory factor analysis method of structural equation to verify these four hypotheses. It is concluded that there is a significant positive relationship between customer value proposition and psychological contract; there is a significant positive correlation between psychological contract and business model innovation; and psychological contract has intermediary effect between value proposition and business model innovation. Furthermore, value proposition has a significant positive correlation with business model innovation, which has not been verified. [ABSTRACT FROM AUTHOR]

In this paper, the algorithm of the deep convolutional neural network is used to conduct in-depth research and analysis of sports health big data, and an intelligent analysis system is designed for the practical process. A convolutional neural network is one of the most popular methods of deep learning today. The convolutional neural network has the feature of local perception, which allows a complete image to be divided into several small parts, by learning the characteristic features of each local part and then merging the local information at the high level to get the full representation information. In this paper, we first apply a convolutional neural network for four classifications of brainwave data and analyze the accuracy and recall of the model. The model is then further optimized to improve its accuracy and is compared with other models to confirm its effectiveness. A demonstration platform of emotional fatigue detection with multimodal data feature fusion was established to realize data acquisition, emotional fatigue detection, and emotion feedback functions. The emotional fatigue detection platform was tested to verify that the proposed model can be used for time-series data feature learning. According to the platform requirement analysis and detailed functional design, the development of each functional module of the platform was completed and system testing was conducted. The big data platform constructed in this study can meet the basic needs of health monitoring for data analysis, which is conducive to the formation of a good situation of orderly and effective interaction among multiple subjects, thus improving the information service level of health monitoring and promoting comprehensive health development. [ABSTRACT FROM AUTHOR]

With the advent of the Internet and the era of big data, education is increasingly dependent on data resources to support product and business innovation, and the lack of data resources has severely limited the areas involved. As a general information filtering method, personalized recommendation systems analyze the historical interaction data between users and items to build user interest models in an environment of "information overload", allowing users to discover and recommend information that interests them. However, the explosive growth of information in the network makes users wander in the sea of information, and it is increasingly difficult to find the information they really need, i.e., information overload. This has given rise to personalized recommendation systems, which currently have more mature applications in industries such as e-commerce, music services, and movie services. To this end, this paper studies and implements a customized educational resource recommendation system that can handle big data. The results show that the values of different similarity calculations all fluctuate with the gradual increase of the number of nearest neighbors, and the algorithm in this paper is maximum at the number of neighbors around 60; then, it is inferred that applying the calculation method to the recommendation algorithm will improve the recommendation accuracy. Therefore, education uses the concept of big data to process the huge amount of education data and find some correlations and laws in education, so as to realize "teaching according to the material, teaching according to the material". [ABSTRACT FROM AUTHOR]

Accurate recommendation of tourist attractions is conducive to improving users' travel efficiency and tourism experience. However, the choice of tourism feature factors and the difference of recommendation algorithm will affect the accuracy of scenic spot recommendation. Aiming at the problems of sparse data, insufficient tourism factors, and low recommendation accuracy in the existing tourism recommendation research, this paper puts forward a scenic spot recommendation method based on microblog data and machine learning by using the characteristics of personalized expression and strong current situation of microblog data and the intelligent prediction function of machine learning, so as to realize accurate and personalized scenic spot recommendation. This paper extracts rich tourism characteristic factors. Typical tourism recommendation algorithms choose tourism characteristic factors from scenic spots, tourists, and other aspects, without considering the travel time, tourism season, and other contextual information of tourists' destination, which can help understand users' tourism preferences from different angles. Aiming at the problem of sparse data and cold start of collaborative filtering recommendation algorithm, this paper introduces deep learning algorithm and combines the proposed multifeature tourism factors to build dynamic scenic spot prediction models (random forest preferred attraction prediction (RFPAP) and neural networks preferred attraction prediction (NNPAP)). The experimental results show that RFPAP and NNPAP methods can overcome the problem of data sparsity and achieve 89.61% and 89.51% accuracy, respectively. RFPAP method is better than NNPAP method and has stronger generalization ability. [ABSTRACT FROM AUTHOR]

The aim of the construction of Northeast Tiger-Leopard National Park is to protect the integrity of endangered wild animals and natural resources. There is a close relationship between land use and ecosystem service value. Improving the quality of ecological environment is the demand of ecological civilization construction. Therefore, this paper aims to evaluate and analyze the ecosystem service value of Tiger and Leopard Park, which is particularly based on big data land use change. Firstly, using ArcGIS software as the support, we have discussed the impact of the dynamic trend of land use change, transfer matrix, and sensitivity coefficient on ecosystem services in different periods of Tiger and Leopard Park. Secondly, we constructed an evaluation model based on the ecological service value method, which is primarily based on geographical conditions and surface coverage. We have completed the value evaluation and change analysis of ecosystem service in Tiger and Leopard Park by analyzing the generation process of ecosystem service data and information characteristics, as well as the ecosystem classification standard of Tiger and Leopard Park and equivalent factor of ecosystem service value per unit area. Simulation experiments show that the method proposed in this paper can reflect the service value of the ecosystem of the Tiger and Leopard Park, lay the foundation for the construction of ecotourism, and promote the green and sustainable development of the regional ecosystem of the Tiger and Leopard Park. [ABSTRACT FROM AUTHOR]

The rapid development of digital economy puts forward theoretical and practical requirements for the construction of legal model of big data transaction management (BDTM). The new problems of ambiguous data ownership, privacy leakage, chaotic digital transaction order, and low nationalization in the context of big data transactions have largely restricted the role of China's big data transaction market and related laws and regulations and are not conducive to promoting the scientific and standardized development of China's digital economy. In this paper, we firstly analyze the connotation, importance, and necessity of the construction of legal model of big data transaction in the era of digital economy from the basic connotation of legal model. Then, we analyze the need to adhere to the principles of systematization, security, and openness in the construction of legal model of big data transaction management and focus on four methods of contract law, market behavior, replication behavior, and dispute resolution in the construction of legal model of big data transaction management. Finally, we summarized the conclusions of this paper. The conclusions of this paper are important for promoting the transformation of digital resources into effective data elements and advancing the scientific and standardized development of China's digital economy. [ABSTRACT FROM AUTHOR]

In recent years, China has been paying more and more attention to the development of the sports industry, and many sports are no longer seen as a mere sport, but can be developed into an industry and play an important role in the development of the economy. This paper examines the application of multi-source big data mining techniques in the analysis of basketball economic management. Firstly, through multi-source big data mining technology, we collect various factors that influence the development of basketball economic industrialization, use Hash Tree-based Apriori algorithm to mine various influencing factors for basketball economic industrialization, and analyze the correlation between each influencing factor. The association rule mining results are then used to analyze the relationship between the key influencing factors and the industrialization of the basketball economy. This paper examines various aspects of the Chinese basketball league market, including the management system, market operations, and talent flow, and compares them with the foreign basketball industry models, in order to analyze the operation of China's basketball industrialization and develop corresponding countermeasures to improve basketball economic management based on the results of the study. [ABSTRACT FROM AUTHOR]

This paper innovates the improvement of the mobile learning model of flipped classroom in English classroom teaching on the basis of big data analysis technology. Through the case study, the big data analysis technology is used to analyze and explore the learning characteristics of students and combine with teachers' teaching experience to dynamically adjust the teachers' teaching strategies. And on the basis of the new model, an assessment model for students, teachers, and school leadership is proposed as a way to build a new and harmonious teacher-student relationship. [ABSTRACT FROM AUTHOR]

With the rise of microblog, social network, mobile APP, location-based services, and other technologies, the global data has exploded. Big data provides a rich source for data mining and analysis, and the results mined from it will become more meaningful and even full of surprises. In this paper, through the problems in processing film and television data, the corresponding model is established to deal with the corresponding problems. According to the related technologies of big data processing and the filtering algorithm of the model, the processing strategies and methods are put forward. The middle also introduces the big data mining method. Finally, according to the different data characteristics and different data processing requirements of the intelligent analysis system of film and television data, comparing the data of film works and TV dramas, the experimental results show that, based on the big data model, the pictures and words in both film works and TV dramas are inseparable and indispensable. In the later research, we choose the appropriate data processing scheme to design and implement the data processing flow, and successfully apply the film and television big data processing strategy and method proposed in this paper to practice, which provides reliable data support for the content analysis of film and television works. The model-based filtering algorithm calculates the relationship between image and text, turns the point into the probability formula of node-to-meta path, and takes the approximate value to achieve efficient model learning. [ABSTRACT FROM AUTHOR]

The development of big data and artificial intelligence ensures the industrial innovation of traditional commodities. Economic transformation and upgrading has become the general trend of social development. Based on the public data of the Rossmann store, a well-known German chain store, and the consumption prediction model of daily necessities based on random forest and GBRT is proposed in this paper. By combining the model to initialize residuals, not only the training speed of the model is effectively improved but also more accurate prediction is obtained. Based on the analysis of the big data, this paper puts forward the influence of daily necessities consumption forecast on the upgrading of industrial structure and constructs a value creation and transmission system for daily necessities, so as to promote the rationalization level of the consumer industrial structure and provide a reference for the upgrading of the same type of commodity industrial structure. [ABSTRACT FROM AUTHOR]

Although conventional business models have been increasingly affected in front of the big data technology application, it has also brought new opportunities and challenges for enterprise development. In order to create a higher value, enterprises should keep pace with the times and actively develop business innovation service models. The greatest value brought by data is to help enterprises find potential business value. It can provide a broader user market and channels, avoid homogeneous competition, and realize the integration of upstream and downstream value chains. In addition, it abandons the extensive development under the traditional model and allows enterprises to return to real value services, which is also an irresistible trend of business model transformation. This paper studies and analyzes business innovation service models. First, the business model as required is presented, and the management system and risk evaluation method are introduced. Then, the construction of the business service model is discussed, and the typical big data technologies are reviewed. Next, according to the evaluation theory of business model, the index system of business innovation service model is explored, which can examine the development of business model objectively and comprehensively. Last, the operations of the business model under the big data are analyzed. The research on the business model in this paper can be provided with universality and has a certain practical value for the development of business innovation service. [ABSTRACT FROM AUTHOR]

With the rise of the sports tourism industry, the group of sports tourism consumers is also growing. The study of the behavior of sports tourism consumers has therefore become a popular topic. This paper firstly organizes and analyzes the research results of scholars, finds out the deficiencies, and finds out the methods suitable for this research. On this basis, this research is practiced, and the methods found are successfully improved to add personal innovation points. Then, the definition and characteristics of cloud computing and mobile big data are elaborated. Next, this paper selects some consumers and their consumption behaviors participating in X city's sports tourism town, golf sports tourism, skiing sports tourism, and outdoor sports tourism projects as the research objects and conducts investigation and research by means of questionnaires and interviews. At the same time, the managers and operators of some sports tourism scenic spots are selected as the research objects, and the investigation and research are carried out through the interview method, which provides a realistic basis for the study of consumer behavior. According to the survey, the proportion of sports tourism consumers who choose to travel in summer is as high as 90.10%. [ABSTRACT FROM AUTHOR]

The term "Digital Photography" has been a relatively new research topic in the field of computer-mediated technology. Although Digital Photography emerged thirty years ago, continuous developments and upgradations are being recorded frequently. Advanced technologies like Digital Technology transformed all the technology into digital mode. The field of photography underwent several developments in terms of camera quality, image quality, image clarity, image transfer, image storage, and other sensitive surfaces. Most of the studies confirmed that digital photography permitted the photographer to record videos at high resolutions. All the developments in the field of Digital Photography have largely contributed to the concept of modern photography and enabled the photographers to produce high-quality images. It also helped photographers enhance their photography skills and accomplish their tasks. All the transformations in the field of Digital Technology happened at a pace, and it was extremely difficult. Therefore, the current paper sheds light on the developmental stages of digital photography. It also highlights the significance of digital photography technology in the era of big data. [ABSTRACT FROM AUTHOR]

In the era of big data, text as an information reserve database is very important, in all walks of life. From humanities research to government decision-making, from precision medicine to quantitative finance, from customer management to marketing, massive text, as one of the most important information carriers, plays an important role everywhere. The text data generated in these practical problems of humanities research, financial industry, marketing, and other fields often has obvious domain characteristics, often containing the professional vocabulary and unique language patterns in these fields and often accompanied by a variety of "noise." Dealing with such texts is a great challenge for the current technical conditions, especially for Chinese texts. A clustering algorithm provides a better solution for text big data information processing. Clustering algorithm is the main body of cluster analysis, K-means algorithm with its implementation principle is simple, low time complexity is widely used in the field of cluster analysis, but its K value needs to be preset, initial clustering center random selection into local optimal solution, other clustering algorithm, such as mean drift clustering, K-means clustering in mining text big data. In view of the problems of the above algorithm, this paper first extracts and analyzes the text big data and then does experiments with the clustering algorithm. Experimental conclusion: by analyzing large-scale text data limited to large-scale and simple data set, the traditional K-means algorithm has low efficiency and reduced accuracy, and the K-means algorithm is susceptible to the influence of initial center and abnormal data. According to the above problems, the K-means cluster analysis algorithm for data sets with large data volumes is analyzed and improved to improve its execution efficiency and accuracy on data sets with large data volume set. Mean shift clustering can be regarded as making many random centers move towards the direction of maximum density gradually, that is, moving their mean centroid continuously according to the probability density of data and finally obtaining multiple maximum density centers. It can also be said that mean shift clustering is a kernel density estimation algorithm. [ABSTRACT FROM AUTHOR]

With the progress of society, the quality requirements of international business enterprises for international business talents have been improved accordingly. So, it is urgent to conduct in-depth research on the teaching model and the improvement of students' practical ability. Taking international business as an example, this paper analyzes the contradiction between the supply and demand of international business technical talents by literature research. Furthermore, the convolution neural network model is used to improve the consistency between the talent cultivation of international business major and the talent demand of enterprises by interviewing teachers and questionnaire survey of students. By studying how to implement the professional training curriculum system construction and enterprise to talented person ability training requirements cohesion, this paper in view of the secondary vocational school of international business in today's society professional training curriculum system was modified and perfected. The ultimate purpose of this paper is to meet the demand of international business for characteristic talents and constantly promote the high-quality development of international business education. [ABSTRACT FROM AUTHOR]

E-payment has penetrated every aspect of people's daily life, and the development and application of E-payment technology have made life more convenient. Besides, the process of E-payment has endless hidden dangers, posing a great threat to payment security. In this context, risk assessment and prediction of E-payment are particularly important. Therefore, this paper mainly studies the E-payment analysis and prediction method based on big data technology. Specifically, this paper uses the BP neural network to extract E-payment data feature and compare and analyze the characteristic of different methods; finally, the payment risks are predicted based on the features, the simulation experiments show that the best result is obtained by the method proposed in this paper, and thus the effectiveness of the new method is verified. [ABSTRACT FROM AUTHOR]

As the world's average population continues to increase, residents' demand for water resources continues to increase. Traditional water resource management models and accounting methods have been unable to adapt to the development of the era of big data. We urgently need to establish a new accounting model to realize the sustainable use of water resources under the sustainable development strategy. Based on the above situation, starting from the development of water resource management and market economy, this paper models and analyzes the water resource accounting of sustainable development strategies in the big data environment. First of all, according to the needs of fine accounting of water resources, we start with the microcontrol principle of accounting. We take the basic principles of the accounting identity and responsibility right generation system as the basic method of water resource accounting. This paper proposes an accounting system framework for water resource accounting accounts and related statements. In the sustainable development strategy, the impact of human wading activities on water resources and ecological environment is discussed. Finally, the application of water basin accounting is analyzed in detail. The results show that under the sustainable development strategy, the water resource accounting model integrated into the market economy development can improve the utilization rate of modern water resources and further improve and realize the concept of sustainable development. [ABSTRACT FROM AUTHOR]

A successful football team not only consists of more than a dozen people on the field but also includes a complete training, analysis, coaching team behind it, and the same basic education and youth training system. With the development of scientific concepts and the advancement of computer technology, more people have begun to study the use of modern technology to replace part of the traditional human work with low creativity and the use of more convenient quantitative analysis, prediction and other technologies to assist football professionals' decision-making. Based on big data and neural network technology, this paper has designed a novel football technical and tactical command decision algorithm. First, the use of big data technology for analyzing the characteristics of the historical big data of football competitions provides valuable data for the work of this article. Secondly, to formulate scientific and reasonable football technical and tactical command, it requires learning effective offensive or defensive strategies from the big data of football competitions. This article uses deep neural networks to learn massive amounts of football competition data, which can effectively predict the offensive and defensive tactics of each position of the team to a certain extent. In addition, in order to better learn the timing video data of football matches, this paper also has proposed to use long- and short-term memory networks to improve the algorithm of this paper. The proposed method has achieved good results in football technical and tactical command and decision-making and also provides some new ideas for the subject of football combined with computer technology. [ABSTRACT FROM AUTHOR]

With the development of economy, all world countries are entering the ranks of competitive sports. The Olympic Games or large-scale gymnastics performances are of great significance to the publicity and implementation of the national health plan. This paper aims to study the computer simulation algorithm of group gymnastics formation change path under artificial intelligence and network big data, so as to find out the path change algorithm of each team member, so that members in group gymnastics can clearly see their own moving route, and finally achieve the purpose of reducing the difficulty and cost of rehearsal. In this paper, the ellipse search algorithm and the traditional D i j k s t r a algorithm, the rectangle search algorithm, and the traditional D i j k s t r a algorithm are deeply compared and analyzed, and then, the experimental comparison between the ellipse search algorithm and the rectangle search algorithm is carried out. It is concluded that when finding the shortest path and when the straight-line distance between two nodes is 4.0 km, the time used by the rectangle search algorithm is 2.1 s, while the ellipse search algorithm needs 3.98 s. This shows that the rectangle search algorithm greatly saves the operation time and has superior performance. [ABSTRACT FROM AUTHOR]

In order to solve the problem of how to explore potential information in massive data and make effective use of it, this paper mainly studies news text clustering and proposes a news clustering algorithm based on improved K -Means. Then, the MapReduce programming model is used to parallelize the TIM- K -Means algorithm, so that it can run on the Hadoop platform. The accuracy and error are used as measurement indicators, and the collected datasets are used for experiments to verify the correctness and effectiveness of the TI value and TIM- K -Means algorithm. In addition, the Alibaba cloud server is used to build the Hadoop cluster, and the feasibility of parallelization transformation of TIM- K -Means algorithm is verified by accelerated comparison. The results show that the parallelized TIM- K -Means has a good acceleration ratio, can save about 30% of the time under the same conditions, and can meet the actual needs of processing massive data in the context of big data. In multidocument automatic summarization, news clustering algorithm can gather the news with the same topic and provide cleaner and accurate data for visual automatic summarization, which is of great significance in the fields of public opinion supervision, hot topic discovery, emergency real-time tracking, and so on. [ABSTRACT FROM AUTHOR]

This paper is aimed at examining the effects of digital teaching on reconstructing creative writing skills and to judge the bettering of creativity and writing skills after accepting digital technology. Based on this paper, creativity and writing skills are the most required elements of creative writing. Fluency, flexibility, originality, and elaboration are the major constructs of creativity. From the contribution of Torrance, we find these constructs of creativity. Narrative and linguistics are the major components in writing skills to evaluate the pupils. Concept and background come under the narrative components as subscales. Likewise, content, the structure of grammar, punctuation, and spelling are considered as subscales in linguistic components. Digital teaching methods and traditional teaching methods are used for this research. We are using a nonequivalent comparison-group design for this research. The rubric is a scoring guide used to evaluate performance. We used this scoring guide to access the improvement of pupils. In total, 58 participants were selected from a primary school. 28 participants were selected from the section A class and 30 pupils from the section B class. In creativity, after the digital teaching, the experimental group is greater than the control group; therefore, the digital teaching method improves the pupils' creativity level. In writing skill, after digital teaching, the experimental group is better than the control group; therefore, the digital teaching method increases the pupils' writing skill. We proposed a SVM-based Chaotic Grasshopper Optimization algorithm for digital teaching of creative writing in the context of big data. The comparative analysis result shows that the SVM-Chaotic Grasshopper Optimization method accuracy rate is higher than the other three methods' accuracy rate. The SVM-CGO method execution time (second) speed is fast when compared with that of the other methods. Thus, we have analyzed the effects of digital teaching and evaluated the improvement in creativity and writing skills after utilizing digital tools. [ABSTRACT FROM AUTHOR]

In order to improve the company's comprehensive budget execution performance and the effect of corporate strategy formulation, this paper integrates the EVA unsupervised learning model to evaluate the company's comprehensive budget execution performance. Moreover, this paper improves the algorithm model by comparing the budgeting process of the company under the traditional budget and the budgeting process of the company under the guidance of EVA. Aiming at the company's massive data, this paper uses combined with the big data audit algorithm to process data and builds a company's comprehensive budget execution performance evaluation system based on the EVA unsupervised learning model. In addition, this paper combines cluster analysis to verify the effect of the system in this paper. The research shows that the company's comprehensive budget execution performance evaluation system based on EVA unsupervised learning model can effectively improve the effect of the corporate financial budget. [ABSTRACT FROM AUTHOR]

In order to improve the teaching effect of modern English, this paper combines the auxiliary and alternative communication systems based on big data analysis to improve English teaching, improve the effect of English teaching, and change the traditional English teaching mode. Moreover, this paper applies the Internet of Things and big data technology to English classroom teaching, and proposes a clustering routing protocol suitable for high dynamic environments. The LEACH algorithm is used for cluster head election and maintenance to ensure the stability of the network. In addition, this paper uses a driver protocol for business transmission between clusters, constructs an auxiliary and alternative communication system based on big data analysis, and evaluates the system in combination with teaching evaluation experiments. According to the teaching evaluation research, the auxiliary and alternative communication system based on big data analysis proposed in this paper has good application effect in English teaching, and can effectively improve the effect of English teaching. [ABSTRACT FROM AUTHOR]

The word "sharing" is not unfamiliar to everyone now, but many people do not understand the sharing of educational resources (ER), and the distribution of ER does not seem to be the topic that people are most concerned about. This paper is aimed at conducting research on the construction of educational big data information intelligence based on resource sharing. A parallel association rule mining approach for educational big data is proposed in this research. Now, the educational big data is classified, and then, the data is operated. At the same time, a CcG-based educational information resource sharing platform has also been built for educational resource sharing, which can be used for cloud download and upload functions of cloud computing (CcG) for teaching and educational resource sharing. The experimental results of this paper show that the educational information resource sharing platform in this paper processes about 150 information per second, and its service performance must be better than that of traditional educational platforms. [ABSTRACT FROM AUTHOR]

Sprint is not only the oldest sport in track and field but also one of the most competitive sports today. This paper studies the characteristics of sprint and the physical characteristics of sports human body. Using the characteristics of genetic programming combined with system structure estimation and parameter estimation, this paper expounds the application of genetic programming in discrete dynamic modeling of complex systems. A model evaluation method based on error estimation is proposed. Based on the analysis of big data, the intelligent control model of track and field sprinters' training progress is designed, and the simulation experiment is carried out through the simulation environment. This paper analyzes their physical quality, discusses the contents and methods of sprint training, and provides a reference for coaches engaged in sprint training. The intelligent training schedule model of track and field sprinters from the perspective of big data analysis has outstanding practical value. It can quickly and accurately formulate training plans, solve data fluctuations under the trend of big data segmentation, and realize intelligent operation. Its fluency and accuracy are very high, which is worthy of vigorous promotion and application. [ABSTRACT FROM AUTHOR]