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Deep learning has extensive application in fault diagnosis regarding the health monitoring of machinery core components. Although deep networks have better nonlinear representation ability, they inevitably introduce a large number of parameters, resulting in slow model training, as well as poor generalization. Additionally, the compound fault tends to be neglected in conventional fault diagnosis. For this purpose, a bearing fault diagnosis method based on mixed pooling deep belief network (MP-DBN) is suggested. Firstly, the Morlet wavelet is adopted for obtaining the corresponding time–frequency representation to enhance analysis efficiency. Then, a new MP-DBN model with a mixed pooling layer and restricted Boltzmann machines (RBM) is constructed to reduce the effects of overfitting and the parameters scale. Finally, the MP-DBN-based method is employed for the bearings in the laboratory and actual working circumstances to verify its performance. The results show that MP-DBN is a powerful compound fault diagnosis technique for rolling bearings with superior feature extraction ability and diagnosis efficiency.