This study investigates the relationship between athlete satisfaction and training performance, incorporating big data analytical techniques to uncover the mechanisms through which various dimensions of satisfaction influence training outcomes. Combining survey questionnaires with dynamic data recording, the study systematically analyzed 500 athletes from basketball, football, and track and field. The results demonstrate that athlete satisfaction plays a critical role in enhancing training performance, with dimensions such as coach support, team atmosphere, and personal achievement significantly impacting athletic performance and psychological states. Using machine learning algorithms, including the random forest model, the study identified key satisfaction factors affecting training performance and validated the direct and indirect pathways through structural equation modeling. Furthermore, the study proposes intervention strategies for optimizing athlete management and training, including improving coaching behaviors, fostering team culture, and implementing personalized training plans. This research enriches the theoretical frameworks of sports management and sports psychology and provides practical guidance for the scientific and intelligent development of modern competitive sports.