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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]

Nowadays, educational data mining technology has received more and more attention from scholars in China, and the application of correlation between student behavior data and student achievement to teaching management has become a hot research topic. Starting from the study of the potential association between book borrowing and student achievement in the big data environment, the paper analyzes the correlation between book borrowing and student achievement based on the Apriori algorithm and concludes that there is a strong correlation rule between book borrowing and student achievement. Based on BP neural network prediction algorithm, the paper constructs an early warning model for student performance by predicting book borrowing through course performance. The absolute value of the error between the predicted value of book borrowing and the real value of borrowing is used as a basis to make early warning for students' performance, so as to realize the monitoring of students' learning situation, thereby providing theoretical suggestions for teachers' teaching and promoting the school's management of students. [ABSTRACT FROM AUTHOR]

During the Ming Dynasty, China and Korea exchanged frequently and recorded a large amount of written information, which is of great value for understanding the culture of that time. The large amount of data makes it difficult to conduct quantitative analysis by researchers, which makes the analysis limited. This paper carries out a research on the cognition and analysis method of Chinese and Korean envoys to foreign cultures in Ming Dynasty based on big data analysis technology. Based on the literature research, this paper determines the ontology model establishment method to efficiently detect the written records of Chinese and Korean envoys in Ming Dynasty. The established ontology model and the improved clustering analysis method can improve the efficiency of data detection, reduce the error of data detection, and provide data basis for the research of this paper. According to the technology of big data analysis, this paper analyzes the focus and status class of Chinese and Korean envoys in Ming Dynasty and analyzes cognition of the Chinese and Korean envoys for the foreign culture. The results show that the envoys of the Chinese and Korean pay different attention to the foreign culture due to their different cognition of the foreign culture, compared with Ming Dynasty envoys, Korean envoys paid 15.3 percent less attention to geography, 19.7 percent more to history, 11.7 percent more to people, and 16 percent less to customs. This reflects the two envoys' different perceptions of the foreign culture. And the status class of the envoys exacerbates this difference. In the early Ming Dynasty, the creative diversity of Ming envoys was far lower than that of Korean envoys. As time went by, the creative diversity of Ming envoys increased. The results provide support for further understanding of Chinese and Korean culture and their relationship in Ming Dynasty. [ABSTRACT FROM AUTHOR]

The implementation of the strategy of rural revitalization is a major ministerial work made by the Nineteenth National Congress of the Communist Party of China and is the general grasp of contemporary agriculture, peasants, and rural work. In recent years, with the rapid development of remote sensing technology and deep learning technology, the demand for the technology for the classification of crops on satellite remote sensing images based on deep learning technology has increased in agricultural insurance and land survey. Therefore, this paper trains one, which is 85.9%–92.8%, the accuracy of corn classification is 77%–93%, and the accuracy of forest classification is 77%–87.6%. Subsequently, the overall accuracy of classifying all directories through the multi-temporal validation data set between May 2017 and October 2017 reached 92.6%. Such a multi-time combination method can be used for monthly, timely, and efficient iteration of agricultural insurance and crop yield estimation, which will be more accurate each time. These methods can also be further applied to the growth and change monitoring of large agricultural planting areas, adding bricks and tiles to China's agricultural remote sensing. If the countryside is to be revitalized, agriculture must develop rapidly at the same time, industries must flourish, ecology must be livable, rural customs must be civilized, and life must be prosperous. Modern agriculture is a comprehensive circulation system with high yield, high quality, low consumption, ecology, environmental protection, and high efficiency. The development of modern agriculture is inseparable from the industrialization of agriculture, the globalization of agriculture, the digitization of agriculture, the integration of agriculture, the adjustment of agricultural structure, and agricultural innovation. Only the continuous development of modern agriculture can make rural revitalization enter a new journey. [ABSTRACT FROM AUTHOR]

English is the most widely used language in the world, and China's economic development has become the second largest country in the world. Therefore, popularizing English in China is the need of national conditions and the desire for talents who are good at English majors. In the current traditional college English education mode and in the external environment, college students' English knowledge has not been substantially expanded, which has seriously caused the social needs not being met and students not being able to show their self-worth. Based on the deficiency of traditional college English teaching mode, this paper designs an efficient and perfect college English education mode by combining information big data fusion technology, so as to ensure that students can really love English and learn English more easily. The results of the experiment are as follows: (1) college English education and students' English learning are analyzed, and the research direction of the experiment is approved, and the functional has been investigated, which lays a foundation for the experimental research of this paper; (2) old-fashioned college English education mode: intelligent big data technology is applied to English education. By using the information big data fusion algorithm to ensure the qualified English teaching effect, it not only helps teachers effectively combine big data resources but also changes the atmosphere of teaching classes, constructs modern language education mode, and promotes students' thinking mode of exploring language learning. [ABSTRACT FROM AUTHOR]

In recent years, the tourism industry has grown rapidly around the world as an emerging force, especially in China, which has become the world's leading tourism country in recent years, and its tourism revenue also occupies a good weight in the country's total income. The number of tourists every year shows a very high growth rate, which not only improves the tourism economy but also brings great pressure to the management of various tourist attractions. In this context, many tourist attractions are actively developing innovative applications of new technologies, hoping to effectively improve their management efficiency. In these experiments, the speech big data analysis technology has achieved remarkable achievements, so it is necessary to combine the speech big data analysis technology with the demand analysis of the tourism industry. The purpose of this paper is to study the effective model of tourism consumer demand prediction using big data analysis. In the process of completing this paper, we consulted a large number of research results of big data analysis technology, tourism-related books, and demand prediction models in HowNet, VIP, and other network databases as well as campus libraries, summarized the related concepts of tourism, and used big data analysis technology to predict the demand of tourism consumers. Understand the needs of tourism consumers on major tourism websites, and extract the indicators that will affect the forecast results of consumer demand, establish a demand forecast model based on the indicators, and analyze its forecast effects through comparative analysis to understand its advantages and disadvantages, in order to establish a tourism demand forecast models providing actionable advice. Through the practical application case of the demand forecasting model, this paper puts forward the development strategy of tourism. The experimental results show that the mean square error of the neural network model is less than 2.5, which is more suitable for predicting the number of tourists, indicating that different models are suitable for predicting different indicators. The main contribution of this research lies in the modeling and analysis of regional tourism characteristics and tourists' willingness, so as to achieve accurate prediction of tourists in scenic spots and formulate targeted plans. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, Internet technology based on big data promotes the formation of digital economy. As a key factor of production in the information age, digital economy has attracted the attention of all countries in the world. In 2017, the CPC Central Committee clearly proposed to build a digital economy with data as the key element. Digital economy is increasingly becoming the mainstream of global economic development. China is in a critical period of economic transformation, and the world is also facing the unprecedented changes in a hundred years. Whether we can seize this mainstream development and push China's economy to a new level is very important to the national economy. Firstly, this paper expounds the development history and significance of big data technology and digital economy, investigates the development status of digital economy in China, classifies digital economy based on big data ecology, and gives the evaluation principles and theoretical basis of digital economy. Finally, this paper explores the influence model of industrial structure on digital economy, combs the empirical analysis ideas of the influence model, and puts forward some relevant suggestions on the development status of digital economy in China. Based on the background of big data statistics, this paper uses structural equation to study the factors that affect the development of digital economy and gives the model empirical ideas, aiming at promoting the development of national digital economy. [ABSTRACT FROM AUTHOR]

Cross-border data flow brings new growth and opportunities for the development of digital economy, but disordered cross-border data flow may damage national security, public interests, enterprise interests, and data sovereignty. At present, the unified rules for global regulation of cross-border data flow have not yet been formed. The existing rules are mainly led by developed countries like Europe and the United States. There is huge room for improvement in the international legal protection of cross-border transmission of personal information. This paper introduces the privacy protection mechanism of personal information data under the digital trade environment in China, that is, the privacy protection framework under the background of big data, cloud computing, and the cloud service selection method of data life cycle privacy protection. At the same time, we combined with many problems existing in the cross-border transmission of personal privacy information in China and compared with foreign advanced experience, and this paper puts forward China's response path to clarify the obligations of data controllers and exporters, improve the responsibilities of regulators, improve the legislation of cross-border data flow, and improve the operability of the law; we vigorously carry out international cooperation and actively participate in the formulation of international rules, so as to further protect the rights and interests of personal privacy information protection. [ABSTRACT FROM AUTHOR]

In the face of global warming, air pollution, and other difficulties, electric vehicles have become an industry strongly supported by various countries due to their good environmental protection characteristics. In the context of big data, people are exposed to more and more information, and the convenience brought by big data is also increasing. Based on this background, the development of green and intelligent vehicles is getting faster and faster. This paper is aimed at studying the application of machine learning algorithms in the development and consumption trends of green and intelligent vehicles in the context of big data. This paper proposes machine learning algorithms based on big data, as well as support vector machine algorithms and so on. Machine learning algorithms specialize in how computers simulate or implement human learning behaviors to acquire new knowledge or skills and to reorganize existing knowledge structures to continuously improve their performance. The test results of this paper show that, starting from 2014, China has begun to vigorously develop green and intelligent vehicles. In 2014, the production volume of green and intelligent vehicles in China was 3,675, and the sales volume was 2,790. The development of green and intelligent vehicles is not very good and has not been fully accepted by the public. However, since 2017, the production and sales of green and intelligent vehicles have been slowly increasing. By 2020, the production of green and intelligent vehicles will be 24,360 and the sales will be 24,090. It can be seen that with the development of time, green and intelligent vehicles are gradually being recognized. [ABSTRACT FROM AUTHOR]

As one of the common languages in the world, English is playing a more and more important role in trade exchanges, cultural exchanges, and other transnational cooperation. In order to better integrate into the world and communicate with the world, China began to vigorously expand English learning as early as the late 1980s. However, with the development trend of economic globalization and cultural diversity, the traditional English ability evaluation methods are not enough to comprehensively measure individual English ability. Therefore, based on this background, this paper introduces the clustering algorithm into English ability evaluation, improves the traditional clustering algorithm represented by K -means on the basis of predecessors, and captures the evaluation data through neural network algorithm combined with the background of modern big data framework. The results show that the fuzzy logic feature mapping clustering algorithm (FLFM) proposed in this paper performs better than the traditional evaluation algorithm, and the evaluation effectiveness and comprehensiveness are improved by about 10 pt. [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]

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]

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]

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]

With the rapid development of information processing, storage, and transmission technologies, the demand for talents from all walks of life in the society has increased, which has put forward higher requirements for the diversity of Chinese higher education. In China's higher education, undergraduate education is in a fundamental position. China's undergraduate education has undergone several reforms in talent training, but there are still many problems in the innovative talent training model. With the gradual improvement of the Internet of Things infrastructure, various applications characterized by big data are deeply embedded in people's daily life. In the era of big data, the amount of data is larger and the structure is more complex. It penetrates into all fields of human life and shows unprecedented vitality and vigor. Especially in the field of higher education, the era of big data provides more information resources for education and learning and also brings new opportunities for the cultivation of applied talents in local undergraduate colleges and universities. This paper deeply studies the training mode of applied talents in local undergraduate colleges and universities under the background of big data and the Internet of Things. Based on the data analysis results of big data application technicians and industry talents, professional ability-course structure data, this paper completes the design of the corresponding talent training mode analysis system. Under the application of the system designed in this paper, it is possible to realize the analysis of the differences in the knowledge structure requirements of talents in various positions and the needs of talents' ability, as well as the optimization and reform of the talent training curriculum system. The design of this paper can play an important guiding role in talent training in the environment of big data and Internet of Things. [ABSTRACT FROM AUTHOR]

With the reshuffle of the global industrial structure, some industrial parks in China are facing problems such as unclear industrial positioning, unclear agglomeration effect, and lack of impetus for independent innovation. It is urgent to seek a way of transformation and upgrading. The arrival of the era of big data also provides new opportunities for the transformation of China's industrial parks. In this context, the model of "character towns" proposed by Zhejiang Province provides a new concept for breaking the bottleneck of industrial park transformation and also provides a platform and space for the application of data technology and digital governance in the era of big data. Based on the theory of creative destruction and self-organization, this paper holds that the essence of industrial park transformation under the concept of character towns is the self-organization process of creative destruction and high-quality reconstruction. After sorting out the development process of parks in China, this paper divides industrial parks into production-oriented parks, consumption-oriented parks, and trade-oriented parks according to the economic activities classified by classical economics. By constructing a six-stage creative destruction model, this paper analyzes the path selection of transformation and upgrading of three types of parks under the concept of character towns and proposes that industrial structure replacement for the production-oriented parks, upgrading the consumption structure for the consumption-oriented parks, and change of trade control points for the trade-oriented parks are the possible transformation paths for the three types of parks. [ABSTRACT FROM AUTHOR]

To explore the pollution characteristics of metals in household dust in China and their exposure to children, this study searched peer‐reviewed papers published during 1980–2020 and analyzed 30 eligible papers screened under the per‐decided strategy. We evaluated the sample‐weighted concentration (SWC) of each metal, explored the sources of metals, and presented the quantitative description of spatial‐temporary characteristics and children exposure to 13 metals with multi‐route under a general living scenario. The results showed the concentrations of 13 metals with a range of 0.89–29 090.19 mg/kg. The SWC of Cd in household dust from rural areas was 3.29 times of that from urban areas, while the SWC of Ni from urban areas was 3.71 times of that from rural areas. The results showed that four principal components were extracted, and the cumulative contribution rate reached 79.127%. The exposure dose of 13 metals to children aged 2–3 years was presented with the highest by ingestion. Metals such as Fe, Zn, and Mn posed inevitable health risk to children with high exposure. Countermeasures should be carried out to minimize the children exposure to metals in household dust urgently, such as the establishment of environmental health standard for household dust. [ABSTRACT FROM AUTHOR]

At present, the e-commerce industry of agricultural products plays a pivotal role in promoting income growth and helping rural revitalization. This paper collected relevant data in the recent 8 years (2012 to 2019) and used the DEA model and Tobit model to analyze the correlation degree between the efficiency and various influencing factors in China. DEA analysis results show that, in recent years, three efficiencies are quite different: the comprehensive efficiency and scale efficiency show an upward trend, while the pure technical efficiency remains at a high level. Tobit model results show that the number of urban Internet users, rural Internet users, logistics practitioners, the development of national economy are negatively correlated with e-commerce efficiency; the length of traffic construction has no significant correlation; the level of agricultural mechanization has a significant positive correlation. Hence, the paper puts forward four suggestions. [ABSTRACT FROM AUTHOR]

In the era of "Internet+," the increase of agricultural products sales can be achieved by creating and joining e-commerce platforms. Most of the farmers in China are self-employed and cannot form an industrial scale. With the rise of "Internet+" background research, the combination of this technology and the marketing of agricultural products has become a new marketing model under the deep integration of different fields. Based on the marketing of products under the background of "Internet+" and using marketing theories, this paper reviews the development of marketing of agricultural products under the background of "Internet+," including the Internet infrastructure, the establishment of logistics system, the formation of branding, and the deep processing of agricultural products. The paper reviews the development of agricultural marketing under the background of "Internet+," including the Internet infrastructure, establishment of logistics system, branding, and deep processing of agricultural products. The marketing mode of agricultural products under the background of "Internet+" is proved, and the development of agricultural products marketing under the background of "Internet+" is discussed from various angles, and insights into how to improve it are proposed from five aspects. After excluding the influence of environmental factors through the improved three-stage SE-DEA model, the mean value of TE of comprehensive efficiency of agricultural information allocation increases from 0.773 to 0.832, the mean value of SE of scale efficiency increases from 0.844 to 0.9219, and the mean value of PTE of pure technical efficiency adjusts from 0.9087 to 0.9058. The mean value of pure technical efficiency (PTE) was adjusted from 0.9087 to 0.9058. Compared with the parametric method, DEA does not require a specific production function to be set in advance, and it is also difficult to select a suitable production function in advance for complex problems. [ABSTRACT FROM AUTHOR]

The development of big data and wireless communication technology has expanded the channels for Western culture to enter China. Diversified teaching forms make us dizzy, which has a great impact on traditional English teaching. This paper aims to study the dilemma and countermeasures of English teaching reform based on multiculturalism under the background of big data and wireless communication. It analyzes the connotation of multiculturalism, the characteristics of the era of big data, the problems existing in college English teaching, and the necessity of college English teaching reform. On the basis of feasibility, in order to understand the current situation of English teaching in colleges and universities, this paper takes teachers and students of a college in this province as the research object, conducts a questionnaire survey on them, and finds out how to better use the background of big data and wireless communication through data analysis. Combined with the multicultural trend of the times to carry out English teaching, the study concluded that under the current technological background, our English teaching must adhere to the student-centered approach and take the improvement of teachers' quality as an important means. [ABSTRACT FROM AUTHOR]

China's economy has grown significantly since the turn of the century, and its domestic situation has become more stable. The employment and employment issues that college graduates encounter across the nation, however, demonstrate the opposite tendency. The general public's attention has turned to "difficulty in getting employment," which has emerged as a crucial component of the overall social employment problem. Additionally, it examines the environment and elements that influence college students' employment, such as the social, economic, and policy elements as well as the university, employer, and individual elements. This study examines the development of college students' entrepreneurial service system by using colleges and universities as the research object and integrating theoretical analysis with empirical research. The M Apriori technique is then parallelized using a Spark and Credible neural network-based approach. This solution makes full use of Spark's benefits based on in-memory processing and data item RDD storage by adopting data parallelism and a local rather than global strategy. The M Apriori algorithm is parallelized and ported to the Spark platform for parallelization, enhancing Spark MLlib. The simulation test and analysis are completed in the end. The study's findings demonstrate the algorithm's excellent accuracy, which is 8.65% greater than that of the Apriori method. Finally, the experimental findings demonstrate the effectiveness of the method used in this paper. [ABSTRACT FROM AUTHOR]

Aiming at the problem of mismatch between talent cultivation and social demand in the process of undergraduate education, this paper proposes a big data-driven method of adaptability analysis and collaborative path mining between vocational undergraduate talent cultivation and social demand. Starting from the big data-driven vocational undergraduate talent training and social needs, this paper points out the problems existing in the current social needs and puts forward the basic framework of vocational undergraduate talent training mode. Secondly, the clustering model of talent training and social demand is analyzed, and the clustering mining method is proposed. Finally, the big data-driven personnel training and social adaptation mining analysis, in their own ability and social needs adaptability analysis, the basic adaptation accounted for a higher proportion. Professional competence has a higher trust value in cluster analysis. Today's social employment situation is becoming more and more severe, and how to enhance the quality of profession undergraduate students has become one of the theoretical and practical issues worthy of attention in China's colleges at this stage. The talent training model of colleges and universities is closely in route with the demands of society, and the problem of student hire is prominent. Therefore, this paper proposes a student employability training program that combines the elements of student employability through social needs research. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet technology today, especially in the fields of new media, digital TV, digital movies, smartphones, smart hardware, mobile Internet and big data, new media forms bring us a shocking sensory experience. Axonometric illustration, a traditional and modern artistic expression, is becoming more and more attractive in the new media era. Today, with the continuous progress of science and technology, the continuous innovation of thinking, and the rapid improvement of public aesthetics, traditional axonometric illustrations have been unable to adapt to the gradual improvement of people's needs for artistic aesthetics. This paper will start from the research background of "new media" and combine the characteristics of the axonometric illustration industry itself. On the basis of analyzing related theories at home and abroad, the literature research method and algorithm-driven method are used comprehensively. It can not only effectively improve the visual effect of axonometric illustration by 12.3% but also solve the problems and challenges faced by the current China's axonometric illustration industry. [ABSTRACT FROM AUTHOR]

With the growth of Chinese economy and the improvement of its international status, Chinese enterprises foreign direct investment has ushered in new development opportunities. Although scale foreign investment continues to expand and has broad development prospects, it also faces huge risks that domestic investment has never had. China and Central Asia are highly complementary economically. Strengthening direct investment in the Central Asian countries can effectually drive China's economic development. Based on the quantitative evaluation and analysis from the perspective of big data, this paper uses a variety of methods to evaluate and explore the risks of China's direct investment in Central Asian countries, and gives some policy suggestions. The research shows that the motivation of Chinese foreign investment cooperation is different from the marginal industry transfer in the traditional international investment theory. The problem of country risks in Central Asia is prominent. Chinese government must rely on the build of the Belt and Road to strengthen intergovernmental communication and exchanges, and establish a good cooperation mechanism to deal with the increasingly prominent problem of country risks. Further we will improve trade facilitation and expand the trade openness of Central Asian countries to China. To avoid risks, Chinese companies should invest under the guidance of the government and establish a complete investment chain for large projects. At the same time, we also need to seize the opportunity of the development of digital economy, and gradually establish a scientific and efficient investment model to effectively avoid the risk of direct investment. [ABSTRACT FROM AUTHOR]

Language is a symbol reflecting ideas and is an important medium for people to realize information transfer and communication. Culture is all the material and spiritual wealth created during the historical development of human society. Language and culture are inseparable. When we learn a language, we must also understand the cultural content it contains. Learning English requires an understanding of the culture of British and American countries, and thus teachers must necessarily teach culture in the English classroom. At present, the software and hardware of Internet technology in our country have been developed in-depth, and the total amount of big data has been in the trend of growth, and China has officially entered the era of big data. This paper takes the reform of teaching mode of British and American culture courses as the main research object and explores how to build a diversified and blended teaching mode based on the existing teaching mode and integrating various online teaching resources under the premise of various technologies in the background of "big data." In order to accelerate the integration and optimization of such course resources, promote the development of theoretical teaching and practical teaching of such courses and improve students' professional ability. [ABSTRACT FROM AUTHOR]

With the rapid development of China's economy and the continuous optimization and improvement of Internet information technology, a new thing, new media, has emerged. On this basis, a kind of artistic behavior of creating, carrying, spreading, and appreciating is also called new media art. New media art has developed for a long time in foreign countries, but its development and popularization are relatively late in China, and it is still in a stage of improvement. As far as visual communication design is concerned, the emergence and development of new media have strongly promoted the development of its work and the dissemination of various works. Developing visual communication design based on new media art has become a key issue for all visual communication designers. Nowadays, deep learning is widely used in different fields in different ways, and image art representation is a hot research field in computer vision. Therefore, this paper mainly studies and analyzes the symbolism of new media art expression forms and proposes a new media art symbolism model based on artificial intelligence big data. In order to provide a method of real-time representation of art images, the deep learning method is used to model the artistic expression of images, and spatial reorganization of content features and style features is carried out. This paper designs simulation experiments to verify the effectiveness of the model, which can provide some help for the development and improvement of new media art in China. We compared the results of our model with a number of other models to test its effectiveness. We achieved a matching accuracy of 98.4% which is higher than or equal to the results of the existing models. [ABSTRACT FROM AUTHOR]

A relevant definition of big data technology has been released by relevant research institutes in China. Big data technology is defined as follows: big data technology is a digitally important productivity factor in the network era; big data technology is based on network technology, statistical technology, and mathematical technology; big data technology, as a carrier of network technology, should be integrated with various industries and technologies other than contemporary ones in order to achieve better informatization and industrialization. In the context of the new crown epidemic in 2020, China's big data technology continues to grow steadily, with a total value of 39.2 trillion yuan, accounting for 38.6% of GDP. This paper investigates the risk constraint in the context of big data and optimizes the benign interaction mechanism of insurance management based on distorted risk metrics in this context. The current situation of insurance risk is investigated through a profound discussion of theories in this paper and through research methods such as distortion risk metrics and SQL Server database management. It is found that the overall situation of insurance industry development in the era of digital economy is not optimistic and so on. And relevant suggestions are made in the conclusion. [ABSTRACT FROM AUTHOR]

Customer churn will cause the value flowing from customers to enterprises to decrease. If customer churn continues to occur, the enterprise will gradually lose its competitive advantage. When the growth of new customers cannot meet the needs of enterprise development, the enterprise will fall into a survival dilemma. Focusing on the customer churn prediction model, this paper takes the telecom industry in China as the research object, establishes a customer churn prediction model by using a logistic regression algorithm based on the big data of high-value customer operation in the telecom industry, effectively identifies the potential churned customers, and then puts forward targeted win-back strategies according to the empirical research results. This paper analyzes the trends and causes of customer churn through data mining algorithms and gives the answers to such questions as how the customer churn occurs, the influencing factors of customer churn, and how enterprises win back churned customers. The results of this paper can better serve the practice of customer relationship management in the telecom industry and provide a reference for the telecom industry to identify high-risk churned customers in advance, enhance customer loyalty and viscosity, maintain "high-value" customers, and continue to provide customers with "value" and reduce the cost of maintaining customers. [ABSTRACT FROM AUTHOR]

With the establishment and perfection of social market economy, China has made changes to the disadvantages of farmers' vocational education system, such as singleness, backward educational means, and backward levels. Compared to traditional forms of farming, problems related to lack of farming expertise, poor scientific and technological awareness, and weak labor skills are analyzed by applying big data mining technology to retrieve key issues in order to establish a professional education system. Data mining can meet the needs of farming knowledge and rapidly develop into an automatic information farming model, which is an effective way to maximize and enhance professional knowledge. The establishment of a professional education system will train most scientific research members and further enhance diversified labor productivity. The experimental summary of this paper is as follows: (1) the relevant data need to be predicted before and after, the predicted experimental data will effectively improve students' grades, which is beneficial to the development of practical teaching, and the pretreatment stage plays a substantial role. (2) Among the three algorithms, the adaptive function of genetic clustering algorithm is obviously better than the other two algorithms, and the adaptive curve is relatively stable. (3) The comprehensive assessment of the course divides students into three categories: poor students, medium students, and excellent students, among which poor students account for 30%, medium students account for 50%, and excellent students account for 20%. (4) The standardization of the education system has brought users a good learning mechanism, in which the teaching resources have been strengthened, and users have very high satisfaction with the evaluation of the whole system. [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]

Quality is strengthened by one's own efforts in continuous learning. Quality education is a requirement for enhancing comprehensive national power. In a sense, it is of great significance to carry out quality education for college students, improve national quality, cultivate innovative talents, and ensure the quality of national and innovative talents, which is a prerequisite for social development. Quality education is the requirement of education reform in higher vocational colleges. Since entering the 21st century, many higher education institutions have slowly changed from elite education to mass education and from professional education in the past to professional education and quality education at the same time. This paper focuses on the study of college students' network quality education system under the big data media management mode, using SQL Server big data management system, Oracle big data analysis research, and related score evaluation method to make an overall analysis of the current situation of China's college students' network quality education, and on this basis, we propose to improve academic standards, improve teachers' quality education ability, follow academic freedom and equality, and improve the suggestions of improving academic standards, improving teachers' quality education ability, following academic freedom and equality, improving teacher appointment system, optimizing evaluation system of quality education results, innovating incentive mechanism of university teachers' online quality education, and creating benign development system of university teachers' online quality education are proposed. [ABSTRACT FROM AUTHOR]

Big Data is the most popular concept in this era, which is the massive amount of information and related technology generated by the information explosion in the era of "Internet+." Big Data is the most popular concept of our time. With the most advanced technology to collect, analyze, organize, and store data, Big Data can effectively handle all kinds of complex information. Because of this, big data is widely favored by all walks of life. In China's sports industry, the use of big data has become mature and has shown its unique advantages. With the development of campus soccer in China in the past decade, how to use big data to promote the sustainable development of campus soccer in China has become a key issue for sports workers to consider today. Based on the above background, this paper proposes a system combining data mining and personalized data recommendation to collect and analyze the information of campus soccer to promote the sustainable development of campus soccer. First, we propose a data mining method based on deep learning data mining network model combined with migration learning to address the data mining problem. The method uses the knowledge of historical model parameters and applies them to new tasks, thus solving the problem of network training when samples are lacking and improving data utilization and data mining effects. Then, for the data recommendation problem, a new deep learning method is proposed, which performs effective intelligent recommendation by pretraining. In the initial phase, the corresponding low-dimensional embedding vectors are learned, which capture information reflecting the relevance of students to soccer sports. During the prediction phase, a feed-forward neural network is used to model the interaction of student and soccer sport information, where the corresponding pretrained representative vectors are used as inputs to the neural network. Finally, it is experimentally verified that the data mining method proposed in this paper can effectively improve the data mining performance and efficiency, and the proposed data recommendation method possesses better accuracy than the traditional methods. The use of this system can effectively collect and analyze campus soccer information, which helps to develop campus soccer and promote the sustainable development of campus soccer. [ABSTRACT FROM AUTHOR]

With the advent of the era of big data, the world is facing the challenge of unprecedented information blowout growth, and Internet information processing technology has also ushered in rapid development. At the same time, charity culture has always been a part that the state and society attach great importance to. It is a form of social wealth redistribution and a supplement to the social security system. Based on this social background and reality, this paper analyzes and explores the psychological changes of charitable cultural behavior by social media based on big data technology. This paper introduces latent semantic analysis and constructs the theoretical framework of social media. This paper introduces the related concepts of heart rate variability and mental state, collects the factor factors and constructs the model, and studies the PLS load and cross load. This paper makes a statistical analysis of the data of charitable donations in China from 2017 to 2021 and tests the validity of the questionnaire from the psychological level. The experimental results show that the correlation between the subscales of the questionnaire and the total scale is between 0.470 and 0.820, and the correlation between the dimensions is between 0.183 and 0.590. The correlation between the dimensions and the total score of the scale is higher than that between the dimensions, which shows that there are both connections and differences between the dimensions of the two scales, which are relatively independent, and the scale has a certain structural validity. [ABSTRACT FROM AUTHOR]

The development of agricultural products e-commerce (APEC) plays an important role in the smooth progress of the rural revitalization strategy. This paper constructs the evaluation index system of the development level of APEC through literature induction, including rural residents, infrastructure conditions, policy support, the quality of APEC platform, operation effect, and social effect. Then, we use the analytic hierarchy process (AHP) to calculate the index weight and take Shanxi Province of China as the research object to evaluate and analyze the development level of APEC. The evaluation results show that the overall development of APEC in Shanxi Province is at a general level. Finally, we put forward some suggestions for the development of APEC, such as improving the standardization system of APEC. [ABSTRACT FROM AUTHOR]

With the booming of big data in finance, data mining technologies, as a new method of data statistics, have made superior economic applications available to researchers. Based on the relationship between shadow banking and interest rate liberalization, this paper intended to analyze the bidirectional relationship between shadow banking and interest rate liberalization using big data mining technologies. The relationship between data mining and the economic application of shadow banking was proven using data from 2014 to 2021. On this basis, this paper identified the bidirectional influence between shadow banking and interest rate liberalization. The findings show that shadow banking has positive contributions to the liberalization of interest rate, and the interest rate distortion resulting from interest rate control also drives the development of shadow banking. Moreover, feasible suggestions have been proposed for supervision on policies. [ABSTRACT FROM AUTHOR]

During the last two decades, rapid development in the network technology has been observed, particularly hardware, and the development of software technology has accelerated, resulting in the launch of a variety of novel products with a wide range of applications. Traditional sports training systems, on the other hand, have a single function and a complex operation that cannot be fully implemented in colleges and universities, causing China's sports training to stagnate for a long time. The goal of physical education and training is to teach a specific action to attain its maximum potential in a variety of ways. As a result, we should use the system to collect scientifically sound and trustworthy data to aid relevant staff in completing their training tasks. Therefore, in the context of big data, network information technology has become the main way to improve the physical education system. By applying cloud computing technology, machine vision technology, and 64-bit machine technology to the physical education training system, extract the video data of the physical education system, design the system video teaching process, and complete the construction of three-dimensional human model, so as to analyze the training situation of the trainers. In this paper, 30 basketball majors in a university are selected as the professional group and 30 computer majors as the control group. The average reaction time, scores, and expert scores of the two groups are analyzed. The results show that the test of the professional group is significantly higher than that of the amateur group. At the same time, the feedback results of students using physical education and training system and normal physical education teaching and training are compared and analyzed. One week later, the students trained by the physical education system have improved their thinking ability, movement accuracy, and judgment ability, indicating that the application of the physical education training system to the actual effect is ideal. [ABSTRACT FROM AUTHOR]

With the promotion of China's international status, the study of Chinese characters has become the trend of scholars all over the world. Nowadays, the international promotion of Chinese has become an important part of the national diplomatic strategy. With the rapid development of big data visualization technology, Chinese culture can be spread quickly, and it is easy to achieve the above requirements to achieve visual accessibility. In view of the problems existing in the current dissemination of Chinese culture, such as too scattered dissemination content, unsystematic dissemination channels, and poor dissemination effect, this paper puts forward a method of Chinese character culture dissemination based on big data visualization. Firstly, the visual features of Chinese characters are recognized, and the rules of Chinese characters are detected; secondly, the large data visualization analysis of Chinese character imaging is carried out, and the performance comparison under different iterations is compared. Chinese visual technology has obvious advantages in the effect and performance of Chinese character transmission. Finally, the development trend of Chinese communication is analyzed, and the acceptance, speed, coverage, understanding, learning degree, and practical degree of Chinese communication abroad are compared. The big data visualization model has certain advantages in functional comparison. [ABSTRACT FROM AUTHOR]

In today's big data era, China's radio and television broadcast volume has reached an unprecedented height, and it is diversified in quality and content richness. The realization of big data model accelerates the transformation of radio and television and constantly reaches new movie-watching heights. In the era when TV dramas, movies, animation, radio stations, and We Media are prevalent, big data is being experimentally analyzed through professional digital technology and effective methods of media integration of radio and television. In order to make China's radio and television industry present a strong industrialization development trend, it is necessary to have a suitable network system to form a pillar, so as to play a substantial role in the development of this industry. Choosing the appropriate evaluation system to evaluate the broadcast volume, ratings, box office volume, and profit income of the mass media is also a breakthrough stage of today's technical ability. The experimental results of this paper show that (1) from the ratings of only 15% in 2010 to 75% today, the successful investment of radio and television in the market has been realized, and the efficient development of modern technology has benefited the people. (2) Department executives are mainly in charge of the economic lifeline of enterprises, and 70% of economic indicators is the embodiment of small workload and high voice of key tasks, which are the main roles in performance evaluation. (3) The accuracy of the old index is only 75 while the new index is 90, so selecting excellent performance indicators is also responsible for performance appraisal. (4) The development of radio and television from urban to rural areas, from 0% of the market to 12.23% of the rural areas, is a manifestation that the development of modern science and technology benefits the whole people. Only when performance evaluation is fed back to the market can it adapt to the next stage of reform and improve the enthusiasm of employees. [ABSTRACT FROM AUTHOR]

"Carbon peak and neutrality" are an important strategic decision to promote the transformation of China's energy economy and build a community with a shared future for mankind. China is a big energy consumer, and the whole society is facing huge challenges under the goal of carbon peak and neutrality. The realization of the goal of carbon peak and neutrality requires the guidance of correct theoretical methods and scientific deployment. This paper mainly studies the energy consumption scheduling, demand response management, and energy trading problems of microgrid. In view of the shortcomings of the existing energy optimization scheduling methods in the microgrid, a variety of energy resources such as electric energy, natural gas, heat energy, and cold energy are considered into the microgrid model. Based on the noncooperative game and Stackelberg game, a new energy optimal dispatch model is constructed with a variety of game methods such as two-layer game. Maximize the personal benefits of the microgrid while meeting the reliable operation of the system and the electricity demand of users. The three-stage noncooperative game problem is solved based on the reverse bootstrap method, and the closed expression of the optimal strategy in each stage is obtained. The power generation forecasting technology based on big data is studied, and a power forecasting method is proposed, which can effectively guide the energy consumption of the microgrid. The simulation results show the effectiveness of the proposed renewable energy management model based on big data, which verifies that the accurate wind power prediction results are conducive to better theoretical analysis of energy management. [ABSTRACT FROM AUTHOR]

With the continuous acceleration of the urbanization process, rural areas have gradually put forward construction plans for new rural forms. Pastoral complex is a construction mode of characteristic townships and rural complexes, which has become a new form of social development today. Pastoral complexes provide a new model of new rural construction, and its development is still in the preliminary stage for China. The construction of pastoral complexes involves rural landscape planning, economic management, etc. It is difficult for rural managers to process these complex data characteristics. Big data technology is affecting people's lives, and the discipline of landscape architecture is also deeply affected by big data. This paper mainly uses data mining technology and neural network method to carry out feature mining and prediction on the landscape planning and management methods of beautiful rural pastoral complexes. In this paper, a data mining algorithm is used to classify the landscape types and locations of rural complex, and the neural network method is used to predict the landscape characteristics. The results show that the methods of data mining and neural network are feasible and reliable both in the proportion of classification and in the prediction of landscape planning. The maximum error of the prediction of landscape design is 2.87%, and the minimum error is only 0.79% using the neural network method, which is an acceptable error range. [ABSTRACT FROM AUTHOR]

In the era of the rise of big data, if insurance companies can effectively and reasonably use existing data to tap more potential information, it can not only improve work efficiency but also achieve precise marketing to customers, thereby saving costs. At the same time, China pays more and more attention to financial insurance, so it is of great significance to use customer information to explore the purchase behavior of financial insurance. This paper mainly analyzes the factors that affect the purchase of financial insurance from the aspects of individuals and families and provides references for insurance marketing. This paper selects the results of the China Comprehensive Social Survey in 2020 as the empirical sample data of this paper and sets whether to purchase financial insurance as the target variable. The characteristic variables were screened, and the 10 variables selected after screening were finally used to build the model. Next, the data are divided into training set and test set, and a decision tree learning model is established on the two data sets at the same time. The classification results of the model are evaluated according to the classification evaluation indicators. Finally, the decision tree analysis results provide suggestions and strategies for the construction of insurance marketing recommendation methods. [ABSTRACT FROM AUTHOR]

Since the twenty-first century, the Internet of Things technology has developed rapidly, and its impact on China's economy and society is far-reaching, and its significance is also far-reaching. This makes more and more scholars start to pay attention to the related issues of the Internet of Things and economic development. The Internet of Things provides a superior information platform for various economic entities, enabling economic exchanges to transcend the boundaries of time and space. The IoT generates a lot of big data, and it is not easy to get valuable insights from the huge amount of data in various formats, ensuring that sensors are working properly and data is transmitted securely and processed efficiently. In order to study the role of the Internet of Things in promoting economic growth, clarify the status of the Internet of Things in the national economy and explore public management innovation as a change in actual social relations. This paper analyzes the application of the Internet of Things technology in public management. In order to deal with the massive multisource heterogeneous data involved in public management, this paper proposes a resource optimization scheduling algorithm for differentiated distributed storage systems and focuses on the analysis of China's economic growth in recent years. Through experiments, it is found that from 2014 to 2020, China's economy will continue to maintain rapid growth, with a GDP of 101,5986 billion yuan. Since 2014, the number and quality of R&D personnel in China have increased significantly. In 2020, due to the resumption of growth in corporate profits in the second half of the year, the cumulative decline in revenue will result in a decline in domestic value-added tax by 8.9% and corporate income tax by 2.4%. [ABSTRACT FROM AUTHOR]

Public sports services (PSS) are an important element in the development of China's public service industry and promoting theoretical research on PSS at the present time has just become an inevitable requirement for the development of sports. The main purpose of the work in this paper is the study of public management data in sport through intelligent big data analysis, with the participation of knowledge graphs, migrating relevant algorithms from other application areas, and optimizing them on this basis. It provides an objective review and analysis of the weaknesses of current sport public management research, etc. Based on informative data sources and in-depth theoretical analysis, it provides an objective description and analysis of the current situation of the development of sport public management services and an outlook on the future development of public sport services. [ABSTRACT FROM AUTHOR]

At present, water-saving standard is an important technical means to implement national water-saving actions and the strictest water resources management system. In order to analyze the development process and research fields of water-saving standards in China, this paper makes quantitative statistics and qualitative analysis on seven aspects of water-saving standards, such as annual publication, drafting unit, research fields, and progress. The results show that the annual release of water-saving standards generally showed a trend of first rising and then declining, and the release reached its peak in 2017. The contribution of China National Institute of Standardization to the development of water-saving standards is dominant. From the point of view of water department, the water-saving standards in the industrial field are comprehensive. Our finding are helpful to better understand the development process of water-saving standards and provide reference for further improving the water-saving standard system and building a water-saving society. [ABSTRACT FROM AUTHOR]

The nursing work in the operating room has the characteristics of long time, strong technicality, and heavy work, which have an important influence on the quality of the operation. Operating room nursing recommendations based on data mining technology can solve a series of practical problems in clinical nursing and nursing management. This paper selects the clustering algorithm in commonly used data mining technology as the research object and actually analyzes the impact of this algorithm in operating room nursing recommendations. At this stage, there is little research on data mining technology in the field of nursing in China. This paper aims to provide new ideas for the field of nursing research by exploring the actual application in the field of nursing. [ABSTRACT FROM AUTHOR]

The application and economic efficiency evaluation mode of traditional composition in the field of modern music cannot meet the needs of various types of music in China, especially Guqin music. Based on this, this paper studies the big data simulation model of Guqin sound synthesis and plucking based on computer composition technology. The computer composition technology uses the discrete dynamic modeling technology of complex system to complete the computer simulation of Guqin sound through the analysis of the correlation between Guqin music data and realizes the storage and analysis of the generated Guqin sound data. In addition, the technology can analyze the data information of composition mode in the piano sound plucking simulation model with the classical Guqin music data stored in the cloud system over the years and then feed it back to relevant professionals for verification. The experimental results show that the Guqin sound simulation model can efficiently compare and analyze the melody and other data of classical Guqin sound with the simulated Guqin sound and can realize secondary data mining. This paper studies the application of computer composition method based on discrete dynamic modeling technology in Guqin sound simulation, which has certain reference significance for improving the cloud data in China's modern music field and the intelligent construction of Guqin sound data cloud storage system. [ABSTRACT FROM AUTHOR]

The agglomeration health output effect of the medical service industry in the era of big data is an important part of the agglomeration innovation of medical resources. This paper used the regression model of data mining to set up the fixed effect model and system GMM model to study the relationship between the agglomeration of medical service industry and resident's health level, based on the panel data of 31 provinces of China from 2003 to 2017. The results show that the health outcome of the medical industrial agglomeration is positive and different in provinces. The influence of medical service cluster on residents' health level in the eastern region fails the significance test, while the medical service cluster in the central and western regions can significantly improve residents' health level. And, this effect is also related to the characteristics of medical resources, economic development, demographic characteristics, and other heterogeneous factors. On this basis, the paper puts forward policy suggestions to promote the market structure of the medical industry from the aspects of strengthening synergies and policy guidance. [ABSTRACT FROM AUTHOR]

The intelligent sensing and communication technology in the airports' grid information system provides a multidimensional big data set for analyzing flight delays. These data from air traffic control, weather, and multiple determinants will cause initial flight delays. Due to the influence of adjacent flight time correlation, the initial delay causes the delay of subsequent flights, discovered by mining information sensing data, forming the phenomenon of flight delay diffusion. Different determinants will lead to the delay diffusion form of different regions, and more seriously, it will lead to "disaster area" delay in the whole regional grid information structure. To analyze the spatial impact of each factor on flight delay and explore the regional distribution of delay determinants, this paper combined the spatial regression model and determined the key explanatory variables by statistical and processing of the aviation system data. The case study showed the spatial airport delay characteristics in terms of aircraft movements in China. After processing intelligent sensing and communication data, the results show that there is a spatial effect between airports in terms of delay and determinants. The high-delay clusters of delay constraints principally occurred in the Beijing-Tianjin-Hebe and Yangtze River Delta urban agglomerations. Direct flights, weather, new flight routes, take-off, and landing capacity have a more critical impact on spatial airport delays. The use of Internet of Things technology to perceive, analyze, and integrate multiple information of airport delay and combine spatial analysis models can accurately mine delay characteristics and effectively achieve digital and intelligent flight delay management. [ABSTRACT FROM AUTHOR]

With the advent of Industry 4.0, economic development has become a rapid information age. The content of macroeconomic forecast is very extensive, and the existence of big data technology can provide the government with multilevel, diversified, and complete information and comprehensively process, integrate, summarize, and classify these pieces of information. This paper forecasts the CPI value in the next 12 months according to the CPI in China in the recent 20 years. Compared with the traditional forecasting methods, the forecasting results have higher accuracy and timeliness. At the same time, the trend of growth rate of industrial value-added is analyzed, and the experiments on MAE and RMSE show that the method proposed in this paper has obvious advantages. It also analyzes the disadvantages of traditional psychological decision-making behavior analysis, introduces the development status and advantages of big data-driven psychological decision-making behavior analysis, and opens up new research ideas for psychological decision-making analysis. [ABSTRACT FROM AUTHOR]