Research on blended online and offline teaching methods for physical education courses based on fuzzy neural networks

This study proposes a hybrid teaching model that integrates a Fuzzy Neural Network (FNN) to improve body movement recognition and optimize exercise intensity in sports education. The model combines fuzzy limb-set representation with a multi-layer neural network to achieve accurate human action recognition, while a physical distance index is incorporated to enhance training sample selection. Experimental findings demonstrate a recognition accuracy of approximately 90.5%, with a maximum false recognition rate of 0.03 (one incorrect recognition in every 30 actions) across eight track-and-field activities. Furthermore, the proposed approach dynamically adjusts exercise intensity according to learners’ performance, resulting in a substantial improvement in students’ health status, increasing from 0.65 to 0.99. These findings suggest that adaptive, hybrid training strategies are more effective than fixed-intensity exercise programs in promoting physical fitness and learning outcomes. The results provide strong support for the application of intelligent teaching models in modern sports education.