Year: 2025
Type: article
Abstract: The IOC is optimizing the 2032 Summer Olympics by integrating sports that reflect modern values and global appeal. To enhance evaluation, we expanded six criteria into 19 indicators assessing popularity, accessibility, and inclusiveness, combining mRMR and hierarchical analysis for a balanced algorithm.For future trends (Problem 3), we applied linear interpolation for missing data, then fused Random Forest, BP neural network, LSTM, and ARIMA via entropy weighting. The model identified Skateboarding, Sport Climbing, and two others as high-potential sports.Problem 4 employed K-means++ clustering to pinpoint emerging sports aligned with public expectations, highlighting Tug of War, Squash, and Skating. Sensitivity analysis (Problem 5) validated k=5 as optimal via the elbow method.Our recommendations to the IOC include:Reforming weightlifting to prioritize athlete health.Adopting data-driven strategies for sport selection.Balancing tradition with innovation to engage younger audiences.This framework ensures the Olympics remain relevant while upholding exclusivity and athlete welfare.
Source: Theoretical and Natural Science
Authors: Haofeng Wang, Yiran Lang, Wenxuan Zhang
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4412494988