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Based on the PSR model, the author proposes a research on news communication. PSR is a model proposed by OECD for system pressure, state and response analysis. On the basis of in-depth analysis of various factors in the process of coordinated development of the social, economic and natural environment composite system, the author constructs a kind of PSR composite model, by using the theory and method of system dynamics, the sustainable development of social, economic and environmental systems is studied, and the knowledge atlas is used to describe and evaluate the current situation and development trend of the education of news communication discipline, conduct a comprehensive and visual analysis of journalism and communication education research to show the frontier, hot spots, knowledge base and cooperation network of the discipline's education research, and try to clarify the relationship and interaction between various studies.

News dissemination is an important way for people to obtain information. With the development of new technologies, traditional news dissemination models have been impacted. It has problems with information filtering and bias, and has certain limitations in news quality, dissemination efficiency, etc., which makes it difficult to effectively meet people's information needs. In order to improve the quality and efficiency of news dissemination, promote the positive impact of news dissemination on society, this article combined artificial intelligence and big data technology to conduct in-depth research on the difficulties and coping strategies of news dissemination. This article first analyzed the characteristics and functions and influencing factors of news dissemination, then provided an overview of the difficulties and coping strategies in news dissemination. Finally, using association rule algorithms, personalized recommendations for news dissemination are achieved. To verify the effectiveness of artificial intelligence and big data in coping with the dilemma of news dissemination, this article conducted experimental analysis from the perspectives of news content quality, dissemination efficiency, objectivity, and dissemination cost. The experimental results show that under the application of news dissemination strategies based on artificial intelligence and big data, the quality of news content and dissemination efficiency have been improved by 4.76 % and 3.63 %, respectively. The conclusion indicates that artificial intelligence and big data can help improve the quality and dissemination efficiency of news content, and meet the diverse needs of the public for information.

This paper gathers information on artificial intelligence, art communication, news communication, and other related topics, with the aim of comprehending the role of artificial intelligence in enhancing information and new media art communication. The paper selects information from the “Annual Development Report of China’s News Industry” and “Review of China’s Advertising Market” to analyze and summarize the current state of the gradual intelligentization of news. We examine the browsing data from new-era news media, including today’s headlines, to investigate the impact of intelligent news dissemination. Lastly, we scrutinize the spatial data from actual new media artworks to comprehend the role of intelligence as a catalyst for experiencing a sense of immersion. The results show that by June 2024, the number of Internet users will be as high as 725 million, accounting for 96.8%. The penetration rate of intelligent new media also rises gradually with changes in time. It reached 84.9% in 2023, and almost half of the users are undergoing intelligent migration. And Tencent News, as a new-age news media, tops the list with a monthly activity of 27,042.17. The interactive nature of artificial intelligence technology promotes artistic communication and broadens the scope of communication.

Emerging technologies such as big data, the Internet of Things and artificial intelligence are developing rapidly and entering into many fields and industries, while their role in the field of news communication and brand image shaping has an important role in influencing. This paper selects WeChat, a Tencent product, as the research object and divides brand image shaping into four dimensions, namely brand awareness, brand trust, brand loyalty, and brand value, to test the relationship between emerging technologies and brand image shaping. Then, we put forward the research hypothesis of this paper and use empirical analysis to test the hypothesis and the empirical results show that the r-value of emerging technology and brand loyalty is 0.591, p

Under the development background of digitalization of big data and information, artificial intelligence and the Internet of Things are rising like mushrooms. It is difficult for the traditional journalism communication teaching mode in colleges and universities to keep pace with the development of the times, and there are several problems in the traditional teaching methods, such as teaching being too rigid, lack of personalized teaching, and teaching means being too backward. This paper focuses on the innovative strategy of journalism communication talents training in the era of full media based on Light GBM model. Firstly, we understand the principle and construction idea of Light GBM model by viewing the information, comparing the difference between XG Boost algorithm and Light GBM two algorithms, analyzing the advantages and limitations of Light GBM, optimizing the algorithm by establishing the hyperparameters of Light GBM model under grid search algorithm, understand the teaching status of college journalism broadcasting majors through survey and analysis, based on Light model under the analysis of feedback to propose targeted innovation strategies, 30 times ten-fold hierarchical cross-validation study of the three algorithms. The mean and standard deviation results of 11 dichotomous classification models accuracy and F1 values under the three algorithms were compared. The results show that in 11 sets of dichotomous data sets, the optimized Light GBM algorithm has 9 sets of experimental accuracy wins and 7 sets of experimental F1 values wins, with 81.8% accuracy and 63.6% of F1 values wins. The optimized Light GBM algorithm had seven experiments that beat the other two groups of algorithms in comparing the accuracy and F1 value. This study can accurately predict students’ learning behaviors to improve the professional performance of journalism and communication students in a targeted manner and has a reference value for the innovation of journalism and communication teaching mode in colleges and universities in the context of big data and all-media era, and this study has great historical significance for the development of Chinese journalism industry.

In this paper, we first study the representation of news text, build the content tree using the CTWE text method, and combine the word vector with the interrelationship between words in the content tree. Then, a feature word extraction technique is applied to filter key information, and Single-pass hierarchical clustering algorithm is used to classify the news content, and a data mining algorithm is applied to realize news delivery. Finally, the impact of information’s initial value and credibility on the transmission process is examined, and the model’s feasibility and practicality are assessed. The results show that the RMSE value of the data mining model is 0.0408, the Pearson correlation coefficient is 0.9334, and the cosine similarity is 0.9596, and the model in this paper has the smallest deviation from the real data and the greatest similarity compared with other models. This study confirms the unique advantages of the data mining model in news dissemination.

The application of Internet of Things (IoT) technology in news media communication has significantly enhanced the effectiveness and coverage of news data releases. However, as the scale of news data continues to grow, traditional IoT approaches face challenges such as slow data processing speed and low mining efficiency. To address these issues, a novel news feature mining system combining IoT and Artificial Intelligence (AI) has been developed. The hardware components of the system include a data collector, a data analyzer, a central controller, and sensors. The GJ-HD data collector is utilized to gather news data. Multiple network interfaces are designed at the device terminal to ensure data extraction from the internal disk in case of device failure. The central controller integrates the MP/MC and DCNF interfaces for seamless information interconnection. In the software aspect of the system, the network transmission protocol of the AI algorithm is embedded, and a communication feature model is constructed. This enables fast and accurate mining of news data communication features. Experimental results demonstrate that the system achieves a mining accuracy of over 98%, enabling efficient processing of news data. Overall, the proposed IoT and AI-based news feature mining system overcomes the limitations of traditional approaches, allowing for efficient and accurate processing of news data in a rapidly expanding digital landscape.

The investigation into entrepreneurial behavior and model innovation of new ventures based on news communication aims to reinforce the market competition strength and improve the performance level of new ventures to meet the urgent needs of transformation and upgrading. Based on the theoretical basis of news communication and social cognition theory, a theoretical model is constructed to analyze the relationship between entrepreneurial behavior, innovation mode, and entrepreneurial self-efficacy (ESE) and to provide a reference for the implementation of entrepreneurial behavior of new ventures. Evidently, taking individual factors as antecedent variables to verify their impact on internal entrepreneurial behavior extends the scope of research on corporate entrepreneurship and also provides significant managerial implications for the promotion of entrepreneurial activities. [ABSTRACT FROM AUTHOR]

The investigation into entrepreneurial behavior and model innovation of new ventures based on news communication aims to reinforce the market competition strength and improve the performance level of new ventures to meet the urgent needs of transformation and upgrading. Based on the theoretical basis of news communication and social cognition theory, a theoretical model is constructed to analyze the relationship between entrepreneurial behavior, innovation mode, and entrepreneurial self-efficacy (ESE) and to provide a reference for the implementation of entrepreneurial behavior of new ventures. Evidently, taking individual factors as antecedent variables to verify their impact on internal entrepreneurial behavior extends the scope of research on corporate entrepreneurship and also provides significant managerial implications for the promotion of entrepreneurial activities.