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Physical fitness refers to the health of all body functions, including cardiorespiratory endurance, muscle strength, flexibility, stamina, and body composition, which can help individuals effectively cope with daily activities and sports challenges. This paper explores the physical characteristics of basketball players, aiming to improve training effects through unique physical evaluation indicators and provide a theoretical framework for improving college basketball performance and training standards. The study adopted the Apriori association rule algorithm in data mining. First, the physical data of basketball players were collected and preprocessed. Then, frequent item sets were extracted through the association rule mining algorithm, association rules were generated, and the key factors affecting the physical performance of athletes were analyzed. The article’s results revealed the potential relationship between different physical characteristics and emphasized the application prospects of association rule mining in the physical evaluation of basketball players.体适能（Physical fitness）指机体各项生理机能的健康状态，涵盖心肺耐力（cardiorespiratory endurance）、肌肉力量、柔韧性、持久力（stamina）以及身体成分（body composition），可帮助个体有效应对日常活动与运动挑战。本研究聚焦篮球运动员的身体特征，旨在通过专属的身体机能评估指标优化训练效果，并为提升高校篮球竞技表现与训练标准提供理论框架。本研究采用数据挖掘（data mining）领域的Apriori关联规则算法：首先收集并预处理篮球运动员的身体机能数据，随后通过关联规则挖掘算法提取频繁项集（frequent item sets），生成关联规则，并对影响运动员身体机能表现的关键因素展开分析。本研究结果揭示了不同身体特质间的潜在关联，并凸显了关联规则挖掘技术在篮球运动员身体机能评估领域的应用前景。