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• W4362576543 abstract "Abstract In recent years, the research on the domestic and international sports industry has made considerable achievements, but there are still some loopholes in the content, that is, there is still a vacancy in the comprehensive quantitative evaluation of the economic development of the sports industry, especially in the statistics of the output value of the sports industry, which lacks a comprehensive understanding of the economic development of the industry. In the state of dual demand research and application, it is still necessary to reveal the publicity and education of industrial scale, industrial structure, industrial function and industrial safety between people and industries, which is also a necessary way to develop the talent guarantee industry. In this work, we should establish an economic model related to the sports industry. It mainly applies the deep learning algorithm and data information mining technology. After allowing the extraction of information from the sports industry database, it is converted into an economic model of the sports industry. It uses scientific and efficient processing methods to analyze a large number of diverse data, in order to find the hidden laws and knowledge behind it. Therefore, this paper uses data mining technology to process and analyze the economic development data of sports industry in detail, and conducts corresponding quantitative analysis according to the requirements of data development. Finally, this paper points out that the neural network in the deep learning algorithm has further training and learning on the economic data of the sports industry, which is convenient for the subsequent prediction of the economic development of the sports industry to make a greater breakthrough." @default.
• W4362576543 created "2023-04-06" @default.
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• W4362576543 date "2023-04-03" @default.
• W4362576543 modified "2023-09-27" @default.
• W4362576543 title "Economic simulation of sports industry based on deep learning algorithm and data mining" @default.
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• W4362576543 doi "https://doi.org/10.21203/rs.3.rs-2725154/v1" @default.
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