RETRACTED ARTICLE: Economic simulation of sports industry based on deep learning algorithm and data mining.

Area of Science:

• Sports Economics
• Data Science
• Industrial Analysis

Background:

• Current sports industry research lacks comprehensive quantitative evaluation of economic development.
• There's a need for better understanding of industrial scale, structure, function, and safety within the sports sector.
• Existing data analysis methods do not fully capture the economic dynamics of the sports industry.

Purpose of the Study:

• To establish a robust economic model for the sports industry.
• To apply deep learning and data mining for analyzing sports industry economic data.
• To improve the quantitative assessment and prediction of the sports industry's economic performance.

Main Methods:

• Information extraction from sports industry databases.
• Development of a sports industry economic model using deep learning algorithms.
• Data mining techniques for processing and analyzing diverse economic datasets.
• Quantitative analysis of sports industry economic development data.

Main Results:

• Identification of hidden patterns and knowledge within sports industry economic data.
• Successful conversion of extracted information into a functional economic model.
• Demonstration of deep learning's capability in analyzing complex economic data.

Conclusions:

• The developed economic model provides a comprehensive quantitative evaluation of the sports industry.
• Deep learning algorithms, particularly neural networks, show significant potential for predicting future economic development in the sports industry.
• This approach facilitates a deeper understanding of the sports industry's economic landscape.