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This study leverages advanced data mining and machine learning techniques to delve deeper into the impact of sports activities on physical health and provide a scientific foundation for informed sports selection and health promotion. Guided by the Elastic Net algorithm, a sports performance assessment model is meticulously constructed. In contrast to the conventional Least Absolute Shrinkage and Selection Operator (Lasso) algorithm, this model seeks to elucidate the factors influencing physical health indicators due to sports activities. Additionally, the incorporation of the Random Forest algorithm facilitates a comprehensive evaluation of sports performance across distinct dimensions: wrestling-type sports, soccer-type sports, skill-based sports, and school physical education. Employing the Top-K criterion for evaluation and juxtaposing it with the high-performance Support Vector Machine (SVM) algorithm, the accuracy is scrutinized under three distinct criteria: Top-3, Top-5, and Top-10. The pivotal innovation of this study resides in the amalgamation of the Elastic Net and Random Forest algorithms, permitting a holistic contemplation of the influencing factors of diverse sports activities on physical health indicators. Through this integrated methodology, the research achieves a more precise assessment of the effects of sports activities, unveiling a range of impacts various sports have on physical health. Consequently, a more refined assessment tool for sports performance detection and health development is established. Capitalizing on the Elastic Net algorithm, this research optimizes model construction during the pivotal feature selection phase, effectively capturing the crucial influencing factors associated with different sports activities. Concurrently, the integration of the Random Forest algorithm augments the predictive prowess of the model, enabling the sports performance assessment model to comprehensively unveil the extent of impact stemming from various sports activities. This study stands as a noteworthy contribution to the arena of sports performance assessment, offering substantial insights and advancements to both sports health and research methodologies.

本研究依托先进的数据挖掘与机器学习技术，深入剖析体育运动对身体健康的影响，为科学遴选运动项目、推进健康促进工作提供坚实的科学依据。本研究以弹性网(Elastic Net)算法为指引，精心构建体育运动表现评估模型。相较于传统的最小绝对收缩与选择算子(Least Absolute Shrinkage and Selection Operator, Lasso)算法，本模型旨在阐明体育运动作用于身体健康指标的影响因素。此外，引入随机森林(Random Forest)算法后，可从四大维度对体育运动表现开展全面评估，分别为摔跤类运动、足球类运动、技能型运动以及学校体育。本研究采用Top-K评估准则，并与高性能支持向量机(Support Vector Machine, SVM)算法进行对照，分别在Top-3、Top-5、Top-10三种评估准则下对模型精度进行验证。本研究的核心创新之处在于融合弹性网与随机森林算法，能够全面考量各类体育运动对身体健康指标的影响因素。通过该集成研究方法，本研究可实现对体育运动作用效果的精准评估，揭示不同体育运动对身体健康的多样影响。据此，本研究构建了更为精细化的体育运动表现检测与健康发展评估工具。依托弹性网算法，本研究在关键的特征选择阶段优化了模型构建流程，有效捕捉与不同体育运动相关的核心影响因素。与此同时，引入随机森林算法可提升模型的预测性能，使体育运动表现评估模型能够全面揭示各类体育运动的影响程度。本研究为体育运动表现评估领域作出了重要贡献，为运动健康研究与研究方法论提供了极具价值的见解与改进方向。