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In view of the weak energy and sparse features of fatigue fault vibration signals of rolling bearings, a fault identification method combining improved adaptive noise complete empirical mode decomposition (ICEEMDAN)and particle swarm optimization least-squares support vector machine was proposed (PSO-LSSVM). Different bearing fault signals are decomposed into a series of inherent modal function (IMF) components by an improved adaptive noise complete empirical mode algorithm; The component that can best represent the original signal state is selected according to the correlation core-variance contribution ratio criterion, and the singular spectrum entropy of the reconstructed component is calculated to form the feature vector; The extracted feature vector set is input into the least square support vector machine classifier based on particle swarm optimization, and the model is trained and the fault mode is identified. The accuracy and efficiency of the model are compared with that of SVM and LSSVM classifier。The test results show that the method can effectively extract fault characteristics from rolling bearing fault signals with an accuracy of 98.75%, which has certain reliability and practicability.