THE AEROBIC EXERCISE RECOGNITION FOR COLLEGE STUDENTS’ PHYSICAL HEALTH STATUS UNDER GENERATIVE ARTIFICIAL INTELLIGENCE

College students face problems of insufficient monitoring accuracy and a lack of personalized training intervention in daily exercise, which restricts the effective improvement of their physical health. To address this challenge, a Self-Attention and Entropy optimized Semi-Supervised Generative Adversarial Network (SAE-GAN) model is constructed to achieve high-precision recognition of aerobic exercise and optimization of training feedback. The SAE-GAN model strengthens local and global dependencies by introducing a feature Self-Attention (SA) module, and reduces category uncertainty by combining information entropy loss, thereby maintaining robust performance with limited labeled samples. Performance test results show that the SAE-GAN model outperforms existing semi-supervised methods under different labeled sample conditions. When there are only 100 labeled samples per category, the recognition accuracy of SAE-GAN reaches 71.7%. Intervention experiments further reveal that personalized training assisted by the model can significantly improve college students’ speed, core strength, and lower limb explosive power. The research results prove that SAE-GAN can maintain high recognition performance under small sample conditions. Meanwhile, SAE-GAN can provide a feasible technical means for precise health intervention and expand the application prospects of artificial intelligence in sports science and college students’ physical fitness improvement.