In view of the difficulty of feature extraction and the low accuracy of fault type recognition in rolling bearing fault diagnosis, a fault diagnosis method based on Improved Complete Ensemble Empirical Mode Decomposition with adaptive noise (ICEEMDAN) and Multi-scale Permutation Entropy (MPE) combined with Aquila Optimizer (AO) to optimize the regularization parameters and kernel parameters of Least Squares Support Vector Machine (LSSVM) is proposed. Firstly, the original vibration signal of the bearing is decomposed by ICEEMDAN. Secondly, according to the double principles of correlation coefficient and variance contribution rate, the eigenmode component (IMF) that meets the standard is selected, and the MPE of the corresponding component is calculated to comprehensively obtain the fault characteristic information; Finally, the multi-dimensional feature vector is formed, and the bearing fault diagnosis is realized by using AO-LSSVM identification model. At the same time, several groups of comparative experiments are carried out. The results show the superiority of the proposed method in rolling bearing fault diagnosis, and the recognition accuracy can reach 98.95%.