Sports Injury Prediction and Prevention: Analysis Methods Based on Big Data and Artificial Intelligence

In recent years, with the improvement of sports competition level, athletes’ physical load has gradually increased, resulting in a significant increase in the incidence of sports injuries. Traditional injury prevention methods mostly rely on empirical judgment, which suffers from high subjectivity and poor accuracy. This article proposes a sports injury prediction model based on big data and artificial intelligence. By integrating deep learning and random forest algorithms, athletes’ physiological data, psychological data, training records and external environmental factors are comprehensively analyzed to improve the accuracy and generalization ability of injury prediction. During the research process, multi-source heterogeneous data were collected and processed by cleaning, normalization and feature engineering. The AUC value of the final model on the test set reached 0.85, showing good prediction effect. Furthermore, personalized injury prevention strategies based on predicted outcomes significantly reduce injury rates in practical applications.