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Aiming at the problem of the samples is difficult to get in rolling bearing fault diagnosis, combining the drawbacks of the LS-SVM model is easy to fall into local optimum, a new bearing fault diagnosis method based on differential evolution and least squares support vector machine is proposed. Firstly, extracting the different fault condition of the bearing vibration signal,Sthen the method of EEM D is used to analyze the bearing vibration signal, get the IMF component, and calculate the correlation dimension and Energy entropy of IMF. These data are used to train the DE-LS SVM model. Using differential evolution algorithm to optimize the structure parameters of LSSVM, improve the diagnostic accuracy of the model. The results show that, compared with non-optimized SVM and PSO-LSSVM, the DE-LSSVM model’s fault classification accuracy and efficiency of diagnosis have been improved, it can be applied to the fault diagnosis of rolling bearings.