Optimization of track and field training methods based on SSA-BP and its effect on athletes' explosive power

Digitalization and informationization are important trends in the development of the sports industry. The study first introduced the sparrow search algorithm to improve the generalization ability of traditional neural networks, optimizing the assignment of initial weights and thresholds of neural networks; Secondly, the chicken swarm algorithm is introduced to optimize the training combination, period, and intensity of athletes based on the evaluation results, improving the subjective limitations of traditional training methods. The results of model performance analysis show that the sparrow search algorithm is better than other intelligent optimization algorithms in finding fitted parameters, and the solution error is less than 0.50. The evaluation model performs well in terms of accuracy, recall, average relative error, and R2 evaluation indicators. The model has high repeatability and is suitable for evaluating track and field training methods. The accuracy and computational speed of the chicken swarm algorithm are relatively good; Compared with other optimization models, the chicken swarm algorithm has better optimization ability and accuracy. Friedman test found significant differences in the chicken swarm algorithm, and the optimized training method has a significant positive impact on the explosive power of athletes, and the training period and intensity arrangement are reasonable and more helpful to the improvement of athletic performance. This study improves the scientific rationality of the development of track and field training methods, which is conducive to optimizing the training effect of track and field sports, and facilitates the risk management and personalized training of athletes. At the same time, it greatly promotes the integration and development of sports and computer disciplines.