Department of Public Teaching, Guangdong Engineering Polytechnic, Guangzhou, Guangdong, China.
PLoS One. 2024 Sep 12;19(9):e0310131. doi: 10.1371/journal.pone.0310131. eCollection 2024.
The article explains the economic dynamics of the sports industry with adoption of deep learning algorithms and data mining methodology. Despite outstanding improvements in research of sports industry, a significant gap prevails with regard to proper quantification of economic benefits of this industry. Therefore, the current research is an attempt to filling this gap by proposing a specific economic model for the sports sector. This paper examines the data of sports industry covering the time span of 2012 to 2022 by using data mining technology for quantitative analyses. Deep learning algorithms and data mining techniques transform the gained information from sports industry databases into sophisticated economic models. The developed model then makes the efficient analysis of diverse datasets for underlying patterns and insights, crucial in realizing the economic trajectory of the industry. The findings of the study reveal the importance of sports industry for economic growth of China. Moreover, the application of deep learning algorithm highlights the importance of continuous learning and training on the economic data from the sports industry. It is, therefore, an entirely novel approach to build up an economic simulation framework using deep learning and data mining, tailored to the intricate dynamics of the sports industry.
本文采用深度学习算法和数据挖掘方法解释了体育产业的经济动态。尽管体育产业的研究取得了显著的进步，但在正确量化该产业的经济效益方面仍存在显著差距。因此，本研究旨在通过为体育部门提出一个特定的经济模型来填补这一空白。本文通过使用数据挖掘技术对 2012 年至 2022 年期间的体育产业数据进行了定量分析。深度学习算法和数据挖掘技术将从体育产业数据库中获得的信息转化为复杂的经济模型。然后，该模型对各种数据集进行有效的分析，以发现潜在的模式和见解，这对了解产业的经济轨迹至关重要。研究结果表明体育产业对中国经济增长的重要性。此外，深度学习算法的应用强调了对体育产业经济数据进行持续学习和培训的重要性。因此，使用深度学习和数据挖掘建立经济模拟框架是一种全新的方法，适用于体育产业复杂的动态。
