In order to improve the accuracy of rolling bearing fault diagnosis effectively, a method of bearing fault diagnosis based on the combination of complete ensemble empirical model decomposition with adaptive noise, bubble entropy and support vector machine is proposed. Firstly, a series of intrinsic modal function components were obtained by CEEMDAN. Then, the important IMF components was chose through the chart and calculate it. Fault feature vectors were constructed and input into the SVM optimized by arithmetic optimization algorithm to train for bearing fault classification. The results show that the accuracy of this method is up to 992% which is 28% higher than that of GASVM. It can also successfully identify the single fault and compound fault of rolling bearing, so it can be used for bearing fault classification.