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In the context of the continuous promotion of China's big data development strategy, this paper quantitively analyses China's existing national-level big data policies from the perspective of policy instruments and coword networks, discusses the rationality of existing policies, explores ways to improve policies, and provides a reference for the innovation of China's big data policies. This paper carries out a quantitative textual analysis of China's national big data policy from the perspective of policy instruments using word frequency analysis to obtain a keyword coword matrix and visualization analysis tools to obtain a coword network. This paper further studies the network node characteristics and structure using social network analysis methods, including degree centrality, clustering analysis, and multidimensional scale analysis, to identify the policy structure and characteristics. Improving big data policy requires improvements in policy instruments on the supply side, resolving existing policy gaps, and strengthening coordination with other policies. [ABSTRACT FROM AUTHOR]

Actively responding to population aging has become the new national condition of China's aging society in the new era. To do a good job of actively coping with aging in the new era, it is necessary to take multiple measures and work together on the basis of clarifying the major realities and problems faced by China's aging society. From the perspective of big data technology, this paper systematically studies the concerns of China's aging through the method of artificial intelligence. In addition, with two typical aging indicators, three coupled intelligent algorithms are introduced in this paper. The prediction results show that the multi-dimensional support vector machine optimized by the immune algorithm shows good advantages compared with the other two algorithms. The multi-dimensional support vector machine optimized by the immune algorithm, one of the big data, has a good prediction effect on the performance indicators of China's aging, which indirectly illustrates the applicability of big data analysis to the concerns of China's aging. [ABSTRACT FROM AUTHOR]

In this paper, the random matrix big data analysis model is thoroughly studied and constructed, the ecological model of college students' innovation and entrepreneurship education is analyzed, and the optimization model of college students' Innovation and entrepreneurship education environmental model based on the random matrix big data analysis model is designed. This paper briefly explains the random matrix and its M-P rate theory deduces the idea of feature extraction by the difference of eigenvalue limit spectrum distribution between different nonrandom matrices and random matrices, gives the data matrix representation method of FEMPL and the specific feature composition basis, and describes the steps of FEMPL feature extraction. A performance model for predicting the running time of Hadoop jobs is constructed using a random matrix. In this paper, innovation and entrepreneurship education has been carried out gradually, and the innovation and entrepreneurship education curriculum, platform, and mechanism have been progressively established. However, there is still a gap between the proper level of innovation and entrepreneurship education development. This study takes education ecology as the research perspective, analyzes the ecosystem of typical schools of innovation and entrepreneurship education, summarizes the dimensions and parameters of the invention and entrepreneurship education ecosystem, constructs an ecological model of innovation and entrepreneurship education for college students, and analyzes the problems and causes of the current innovation and entrepreneurship education ecology for college students based on the model, to propose specific strategies to promote the ecological development of innovation and entrepreneurship education for college students. [ABSTRACT FROM AUTHOR]

For smart city traffic flow prediction in the period of big data and industry 4.0, the prediction accuracy is low, the prediction is difficult, and the prediction effect is different in different geographical locations. This paper proposes a smart city traffic communication forecast based on Industry 4.0 and big data analysis application. Firstly, this paper theoretically explains the application scenario of urban traffic fault text big data and analyzes the characteristics of related problems, especially the fault problems. Secondly, the AC traffic prediction algorithm is studied, and the application analysis of PVHH, IDT, and Ford–Fulkerson algorithms is applied, respectively. Finally, the above three algorithms are used to predict and analyze traffic flow. [ABSTRACT FROM AUTHOR]

Due to the continuous progress of the era of mega data, it is more and more important to carry out accurate marketing of online video. Based on the analysis of users' big data, this paper proposes and implements an online video precision marketing algorithm, which provides a reference for the precision marketing strategy of video websites. This paper introduces the concept and characteristics of precision marketing and analyzes the application forms of precision marketing of video websites. It also summarizes the operation system, operation mode of precision marketing, and the influence of online video advertising on enterprises and users. In order to realize the accurate marketing of big data, it needs to be carried out from three parts: data collection, data analysis, and marketing plan formulation and implementation. Through continuously collecting customer data, it gradually and clearly acquires the characteristics of customers and constantly dynamically adjusts the marketing strategy to customers, so as to accurately conduct video marketing to customers and greatly improve the marketing efficiency. In order to test the effectiveness of the marketing algorithm in this paper, we conducted an experiment. The research in this paper has important practical value and reference significance for reducing the cost of video advertising of enterprises and improving the communication effect of online video advertising. [ABSTRACT FROM AUTHOR]

This paper uses big data technology to predict the employment rate of colleges and universities. In this paper, combined with the current rental price, daily life consumption, and college students' personal interests and hobbies consumption and other indicators, the individual is simulated by big data, and the individual is associated by using the AI-driven edge fog computing service optimization algorithm to form a cluster, so as to realize the prediction from element to neural network cluster by using edge computing. In addition, this paper takes the employment data of colleges and universities in Hunan province from June 2020 to May 2021 as the research sample to test the prediction model and makes a comparative analysis with the CNN model and LSTM model. The edge fog computing model in this paper has more analytical indexes as tuples compared to the CNN model, so the results show that the prediction accuracy can reach 83.25%. In this case, there is little difference between the two models of data processing and predictive efficiency. Compared with the LSTM based classification prediction model, this model is edge computing, which greatly improves the data quality of model and data parameters, and the calculation efficiency can be increased by 45%–65%. Therefore, the use of big data technology can provide a reference for the research direction of higher education. [ABSTRACT FROM AUTHOR]

Along with the development and promotion of Internet technology, new media are increasingly diversified, enriching and changing our lives. This paper focuses on outdoor interactive advertising as a communication method, analyzing its creative features and applications. Unlike traditional advertising, where audiences receive passively, new media interactive advertising establishes a more direct communication method for consumers and products, creating different sensory stimulation and emotional experience environments according to the characteristics of products and conducting a series of interactions with consumers' sensory systems such as vision, hearing, smell, taste, and touch. The advertising method has also changed from passive reception to active participation. Finally, combining the characteristics of accurate delivery and the diversity of presentation forms of interactive advertising under the big data platform, the theme conception and form design of interactive advertising is proposed. The interactive design principles of highlighting the simplicity of the theme are explored. The interactive design principle of highlighting the simplicity of the theme is proposed, and the combination of color, text, and graphics is explored by the aesthetic visual design of the audience and the premise of focusing on the audience's feelings. Starting from the visual effect of outdoor interactive advertising, the change in the way advertising is conveyed is interpreting to us the arrival of a new communication concept, the arrival of the Internet-centered and technology-centered data integration era. The research in this paper aims to provide useful support for the diversified development of outdoor interactive advertising in the new media environment by exploring how to skillfully realize the creative expression of outdoor interactive advertising in the new media environment. [ABSTRACT FROM AUTHOR]

This article first addresses the problem that the unstructured data in the existing e-learning education data is difficult to effectively use and the problem that the coarser granularity of sentiment analysis results in traditional sentiment analysis methods and proposes multipolarized sentiment based on fine-grained sentiment analysis evaluation model. Then, an algorithm for behavior prediction and course recommendation based on emotional change trends is proposed, and the established multiple linear regression equation is solved with an improved algorithm. Finally, the method in this paper is verified by a comprehensive example with algorithm comparison analysis and cross-validation evaluation method. The research method proposed in this article provides new research ideas for evaluating and predicting the learning behavior of e-learners, which is conducive to timely discovering learners' dropout tendency and recommending relevant courses of interest to improve their graduation rate, so as to optimize the learning experience of learners, promote the development of personalized education and effective teaching of the e-learning teaching platform, and provide a certain reference value for accelerating the reform process of education informatization. In order to improve the speed of searching for parameters and the best parameters, this paper proposes a particle swarm algorithm (to improve the support vector machine parameters in a sense) and finds the best parameters which also achieved the goal from academic expression to academic performance. [ABSTRACT FROM AUTHOR]

It is urgent to effectively monitor the public opinion of the news communication platform. The platform designed in this paper takes microblog public opinion as the research goal, uses MongoDB to build a distributed computing platform for sensitive information of news communication platform, establishes a corpus of sensitive event topics, introduces PageRank algorithm to deal with microblog social relations, obtains the characteristics of sensitive information of news communication platform, and carries out information screening, so as to accurately screen and mine the keywords in high impact information. To ensure the practical application effect of sensitive information mining method of news communication platform based on big data analysis. Finally, the experiment proves that the sensitive information mining method of news communication platform based on big data analysis has the advantages of high timeliness and high accuracy, which fully meets the research requirements. This is fully in line with the requirements 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]

This paper conducts an in-depth research analysis on the precise employment of college graduates in the context of big data using a number-driven approach. The textual information of the study is obtained by using in-depth interviews, and the evaluation index system of college students' employment quality is constructed by combining the step-by-step coding method with rooting theory. The research on the current situation of employment recommendation platform research and the application status of big data in the employment recommendation platform is explored by using a bibliometric approach. And the innovative use of web crawler technology is used to comprehensively understand the recommendation function and status quo of the same type of recommendation platform, which provides a reference for the research of this platform. Based on the preliminary analysis of platform requirements and overall design, the overall design and functional implementation of the big data employment recommendation platform are carried out by using big data crawler technology, big data architecture technology, text mining technology, database technology, etc. The construction of a recommendation module based on user history information, a recommendation based on real-time user online behavior data, and hybrid recommendation carried out on the recommendation module to grasp all-round the platform is built based on a stakeholder perspective. Based on the platform construction, the initial platform operation and maintenance management mechanism was established from the stakeholder's perspective. The Pearson correlation coefficient is used to objectively evaluate the current situation of talent supply in universities and talent demand in enterprises from the perspective of image and data. In the research on the development status of the big data education industry, the Lorenz curve and Gini coefficient are used to match the status of new big data majors with their college construction volume in each province and provide data support for the reasonable adjustment of majors setting in each province according to the education level. [ABSTRACT FROM AUTHOR]

This paper provides an in-depth analysis and study of the interactive flipped classroom model for a digital micro-video for a big data English course. To improve the learning efficiency of English courses and reduce the learning pressure of students, the thesis also uses certain techniques to apply audiovisual language to the production of specific micro-class videos, broadcast the successfully recorded micro-class courses to students, and then use the questionnaire to randomly distribute the designed audiovisual language use questionnaire. Micro-classes earnestly perform data statistics for students and finally conduct data analysis to summarize and verify the effects of micro-class audiovisual language use. The improved algorithm can effectively reduce the fluctuation of the consumption of various resources in the cluster and make the services in the cluster more stable. The new distributed interprocess communication based on protocol and serialization technology is more efficient than traditional communication based on protocol standards, reduces bandwidth consumption in the cluster, and improves the throughput of each node in the cluster. The content design and scripting of micro-video teaching resources are based on this. Then, the production process of micro-video teaching resources is explained, according to the selection of tools, the preparation, recording, editing, and generation of materials. [ABSTRACT FROM AUTHOR]

In this paper, the fuzzy comprehensive evaluation model based on the bat algorithm quantifies the qualitative evaluation effectively and provides a feasible and convenient English teaching quality evaluation system by combining quantitative evaluation with objective index data. Firstly, the English teaching quality evaluation model is constructed based on the fuzzy comprehensive evaluation analysis method and the weight values of each factor are calculated; secondly, the three types of data in the model are processed separately. This includes standardizing the data of objective indicators such as students' course grades and weakening the influence of course difficulty on this indicator. The fuzzy comprehensive evaluation model based on the bat algorithm quantifies the qualitative evaluation to make the calculated comprehensive evaluation of English teaching quality more comprehensive and objective; then the comprehensive calculation of English teaching quality evaluation is completed, and the English teaching quality evaluation model is constructed by extracting keywords based on the qualitative evaluation; finally, a runnable English teaching quality evaluation system is designed and implemented. A fuzzy comprehensive evaluation algorithm based on improved bat algorithm optimization is proposed. The algorithm uses the improved fuzzy comprehensive evaluation algorithm to optimize the initial clustering centers and adopts a new objective function to guide the clustering process, thus improving the clustering quality of the fuzzy comprehensive evaluation algorithm. Comparative analysis through models shows that the improved algorithm improves the clustering accuracy to a certain extent when compared with the traditional fuzzy comprehensive evaluation clustering algorithm for analysis. The bat algorithm is one of the stochastic global optimization models. It can take advantage of the group, integrate global search and local search, and achieve rapid convergence. Therefore, it plays an important role in optimizing the evaluation of English teaching quality. This study enriches the theoretical study of English teaching quality evaluation to a certain extent and can play a role in strengthening and improving English teaching quality evaluation at the present stage. [ABSTRACT FROM AUTHOR]

In this paper, a hierarchical neural network power source model is used to conduct an in-depth analysis and research on human capital technology innovation and revenue distribution. A hierarchical neural network analysis method was chosen to evaluate the human capital value of professional degree master students, and the applicability of the index system was confirmed through errors; moreover, the significance of the output results was analyzed according to the weight assignments of the input, implicit, and output layers. The analysis found that there was a large disagreement in the assessment of their human capital value, which led to the lack of practical utility of human capital. Knowledge-skilled talents have a wealth of theoretical knowledge and can use theories to guide related work. Compared with technically skilled high-skilled talents, their educational level is higher, and they can summarize past intuitive experience into theoretical guidance. Therefore, the hierarchical neural network method we constructed is theoretically effective in assessing the value of the human capital of professional master's students and the role of the main constituents. Based on the assessment results, we can provide policy-informed suggestions for improving the quality of school education. To quickly verify whether the model can converge during the training process, a simple dataset with only two sequences and the elements in the sequences being real numbers rather than vectors are constructed to speed up the computation; meanwhile, the length of the sequences in this dataset is adjustable to initially verify the model's ability to alleviate the long-time dependence problem. [ABSTRACT FROM AUTHOR]