The service information system is constantly transforming to a networked information model, and domestic hardware equipment is constantly updated. Independent controllability has also become the basic requirement of the new information age. With the development of the information age and the new era of independent control, more and more services and applications will also be deployed on autonomous and controllable cloud platforms. With the rapid development of Internet technology in the information age and the resulting changes in productivity, people can record, store, and transmit more and more information. When information becomes recordable, storage, and easy to transmit, information becomes modern meaning nowadays, an era of information explosion characterized by massive, volatile, timely transmission, and diverse forms has truly come, forming what is now called the “big data era”. This article mainly introduces the analysis of sports big data based on the cloud platform and the research on the impact on the sports economy and intends to provide ideas and directions for the analysis of sports big data and the research on the impact on the sports economy. This paper proposes a cloud platform-based sports big data analysis and research methods for its impact on the sports economy, including the use of Hadoop cloud platform big data processing systems and support vector regression algorithms for cloud platform-based sports big data analysis and sports economy. The experimental results of this paper show that the average correlation between sports big data analysis and sports economic development is 0.5155, and appropriate cloud platform-based sports big data analysis plays a positive role in promoting sports economic development.
The rapid development of information technology and Internet makes the sports information resources retrieval service more convenient and quick; sports policy in recent years lays a foundation for the development of the Internet + sports, the development of sports industry in the process of our country economy level of development status, and the development of sports industry into the era of information and big data. This paper takes OpenStack cloud platform as the research basis (1) to realize the sharing of sports industry information resources in OpenStack cloud technology and (2) to realize big data analysis of sports industry and (3) empirical research on big data of sports industry. The main content is to realize the construction of sports resources informatization based on the OpenStack cloud platform. Through the analysis and empirical study of the big data of the sports industry, the influence of the development of the sports industry in the process of China’s economic development is discussed. In this paper, the experimental results show that the sports industry showed a positive impact in the process of economic development, the sports economy for the development of the economy, the contribution rate reached 11.77%, the sports industry for the development of the economy, the pull rate of 1.056%, based on the cloud platform of information resources sharing of data analysis, sports industry for the development of the economy has a positive role in promoting.
With the development of information technology, high-speed transmission of electronic information resources, and explosive growth of information data, traditional data processing cannot meet the development of the times. With the arrival of the digital age, massive virtual numbers are deeply analyzed to turn the data into understandable information. As a product of the information age, sports app is widely used. With the increase of sports apps, the development of sports apps also has a bottleneck. In this paper, the survey data of sports app users are analyzed and processed by Excel 2016 and SPSS 23.0: (1) descriptive statistics of the research objects through technical statistical analysis; (2) testing the validity and credibility of the data through exploratory factor analysis and confirmatory factor analysis; (3) analysis of variance performed by independent sample t-test, one-way ANOVA, and Duncan’s test; (4) analyzing the correlation between factors through correlation analysis; (5) analyzing the influence among factors through multiple regression analysis. The results show that, through variance analysis, sports app users have significant differences in demographic characteristics ( P < 0.05 ). Through correlation analysis, the participation motivation, satisfaction, and loyalty of sports app users are highly correlated ( P < 0.01 ). Through multiple regression analyses, the participation motivation, satisfaction, and loyalty of sports app users have a significant positive impact ( P < 0.001 ). The purpose of this study is to provide theoretical references for the related research and decision-making of sports apps.
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