Sports Economic Mining Algorithm Based on Association Analysis and Big Data Model.

Area of Science:

• Sports Economics
• Data Science
• Management Science

Background:

• National strategies emphasize sports development, increasing demand for sports economy and management professionals.
• The rise of big data necessitates new approaches in sports economics education and practice.
• Massive multisource spatiotemporal data generation requires advanced analytical techniques.

Purpose of the Study:

• To explore the application of correlation analysis and big data in the sports economy.
• To propose and validate a sports economy mining algorithm based on big data models.
• To enhance the fine management and development of the sports economy.

Main Methods:

• Utilized correlation analysis to establish relationships between sports economy data and big data.
• Developed a sports economy mining algorithm integrating correlation analysis and big data models.
• Employed experimental verification to assess the proposed model's effectiveness.

Main Results:

• The proposed algorithm effectively mines sports economy data using correlation analysis.
• Analysis of big data reveals key issues in sports economic development and enables fine management.
• The study demonstrates the significant role of multisource big data in the evolving sports business landscape.

Conclusions:

• Mastering big data acquisition, analysis, and application is fundamental for sports economic analysis.
• The developed algorithm provides a foundation for data-driven decision-making in the sports economy.
• The integration of big data and advanced analytics, including virtual reality, is a key trend in sports business.