Bearings are critical components of rotary machinery equipment. Numerous studies have been conducted on bearing fault diagnosis.Some of these methods can only be used for diagnosis of a certain type of bearing failure and cannot detect other failures. The diagnostic accuracy rate for most methods can be further improved. A new method is proposed for bearing fault diagnos is based on wavelet packet energy entropy and deep belief network (DBN).The bearing vibration signal is processed using wavelet packet transform to get the energy entropy feature vector. The feature vector represents the vibration energy in different frequency bands, which can be used to distinguish the fault type. The deep model based on DBN is adopted to recognize fault types.The proposed method achieves 100% and 995% fault recognition accuracy on two bearing datasets, respectively.The experimental results show that the proposed method has good versatility and can achieve high diagnostic accuracy.