Rolling bearing is one of the important parts of rotating machine. Aiming at the problem of rolling bearing fault diagnosis, this paper proposes an algorithm combining multiscale permutation entropy (MPE) and support vector machine(SVM) optimized by particle swarm optimization (PSO). The fault characteristics of the bearing fault data was obtained by the MPE method, fitting as a feature vector into the PSO-SVM model, using Case Western Reserve University bearing dataset for verification. It is found that this method can effectively identify the fault of the rolling bearing. This method is compared with the fault classification results obtained by combining the multi-scale permutation entropy with the traditional SVM method and the SVM method optimized by grid search. It is found that the method proposed in this paper has certain advantages in the efficiency and accuracy of rolling bearing fault diagnosis.