The objective of this paper is to enhance the operational efficiency and stability of sports event management through the refinement of a data mining algorithm, specifically an adaptation of the fuzzy C-means clustering method. This study addresses the practical challenges of sports event management by developing a system architecture that aligns with the specific functional requirements of such events. The system employs a point-to-multipoint bridge model, leveraging the campus network infrastructure to deploy server-side applications, thereby enabling access to the information management system via any browser-equipped device on both the campus and external networks. Furthermore, this research introduces a sports event management system that harnesses the capabilities of big data technology. The performance of this system has been rigorously evaluated, and the experimental results indicate its efficacy. Consequently, the system presents a viable solution for management in forthcoming sports competitions.