Aiming at the problem of feature extraction and fault diagnosis of rolling bearing vibration signals, a fault diagnosis method of rolling bearing based on wavelet packet information entropy and support vector machine (SVM) optimized by snake optimization algorithm (SO) is proposed. The collected vibration signals are processed by using the wavelet packet, the energy spectrum entropy and the coefficient entropy of the wavelet packet are constructed, and the constructed feature vectors are input into the SO-SVM for identification and classification; Finally, the multi-fault pattern recognition is realized and the diagnosis results are output. The simulation results show that the diagnostic accuracy of this method for five different groups of samples reaches 99.17%~100%, and compared with FOA-SVM and PSO-SVM, it has a higher effect of fault recognition and classification.