Design and Development of Intelligent Optimization-Driven AI Framework for Enhanced Higher Vocational Physical Training Assistance

The swift advancement of artificial intelligence has allowed for the creation of intelligent support frameworks that maximize the efficiency of physical training in higher vocational education through the inclusion of optimizationbased learning mechanisms. The current systems tend to lack adaptation flexibility, personalized feedback, data processing inefficiency, and poor generalization to realistic training situations, whereas conventional methods like rule-based evaluation or surface learning models are incapable of identifying intricate biomechanical and performance-based patterns. To address these constraints, the suggested hybrid attention deep neural network optimized by particle swarm optimization (PSO) is developed to dynamically mine significant features, capture complex temporal-spatial relationships, and optimize parameters for best predictive accuracy. Experimental testing shows significant improvements over traditional deep learning and heuristic-based platforms, registering 97.8% accuracy and 96.9 % F1-score, outperforming benchmarks by margins of more than 5 % in accuracy and 6 % in F1-score. The superiority mechanism is based on the synergistic combination of attention-driven representation learning and PSO-based parameter optimization for robust adaptability, convergence acceleration, and high generalization with diverse physical training datasets.