Enhancing Informatization in Ice and Snow Sports Education Through Biological Principles and Hybrid Clustering Models

The rapid expansion of ice and snow sports has highlighted critical challenges in the design and delivery of specialized curricula. Traditional teaching models often fall short due to fragmented structures, inconsistent resource allocation, and a lack of personalized, data-informed instructional strategies. This study proposes an intelligent, biologically-informed framework for optimizing ice and snow sports education by integrating innovation and entrepreneurship education, clustering algorithms, and big data analytics. We first explore the application of fuzzy c-means (FCM), optimized K-means, and hybrid metaheuristic algorithms in the evaluation and classification of informatization levels across specialized sports schools. Data were collected through nationwide surveys and processed using orthogonal matching pursuit and min-max normalization for standardization. Results from clustering and dimensionality reduction reveal three distinct levels of informatization application, supporting a more tailored educational model. Moreover, by embedding biological principles—such as individualized performance feedback and recovery analysis—into curriculum evaluation, the study enhances personalization and ecological adaptability in sports education.