Abstract: Aiming at the problem of fault diagnosis of low-speed bearing, an acoustic emission diagnosis method based on the combination of complementary ensemble empirical mode decomposition (CEEMD) energy entropy and support vector machine (SVM) is proposed. Firstly, the acoustic emission signals of bearing with different damage states are decomposed by CEEMD, thus an adaptive intrinsic mode component (IMF) is obtained. Afterwards, the combination of the variance contribution rate and IMF component mutual correlation coefficient is used to remove the false component and to sift out effective component for signal reconstruction. Due to the different energy distributions of different damage bearing, the damage state of the bearing can be characterized by the change of energy entropy. The energy entropy of the extracted effective IMF components is calculated as the feature vector of different fault bearing. The feature vector is input to the support vector machine to classify and identify the different faults. The experimental results show that the correlation coefficient and variance contribution rate can be selected with the main fault information of the IMF component. At the same time, it is proven that SVM is better than BP neural network in identifying different fault types of low speed bearings.