When using the Random Forest (RF) algorithm to estimate the road adhesion coefficient, there are issues such as insufficient optimization of feature selection during model construction and insufficient diversity in the ensemble of decision trees. To address this issue, a method based on Particle Swarm Optimization (PSO) algorithm to improve RF is proposed, and the algorithmic process is presented. An RF model for estimating the road adhesion coefficient is established, and the PSO algorithm is used to optimize the parameter configuration of RF, including key factors such as the number of features of each tree and the number of trees, so as to enhance the diversity and generalization capabilities of the model. At last, a joint simulation model is built on the MATLAB/Simulink platform for experiments. The comparative experimental results show that the random forest road adhesion coefficient estimation method based on PSO-RF can overcome the limitations of the traditional RF methods, and both the estimation accuracy and stability have been significantly improved.