In this study, a model combining Particle Swarm Optimization (PSO) and Random Forest Algorithm (RF) is proposed for processing and selecting features in a sports competition dataset and optimizing the model parameters. First, the scale differences between different features were eliminated by data normalization, followed by optimizing the parameters of the random forest model using the PSO algorithm, which significantly improved the prediction accuracy of the model. The study also introduced the performance level …