Electrical current signal possesses the characteristics of being easy to collect and not easily affected by environmental noise, which provides a feasible monitoring and diagnosis idea for the special equipment that is difficult to collect signals with vibration sensor. However, the electrical current signal also has the problems of being difficult to extract fault features. An intelligent fault diagnosis method for mechanical equipment based on electrical current signal is proposed, which combines the improved dynamic statistical filtering and deep convolution neural network (DCNN). In order to improve the accuracy of state recognition, the integrated information quantity index (SIpq) is introduced to optimize the filtering effect, which can maximize the difference of the features among different state signals based on dynamic statistical filtering. Through alternately stacking the convolution layer with invariant size of the feature map and the pooling layer with decreased the size of the feature map layer by layer, the DCNN is constructed to extract the high dimension fault features in the electrical current signal step by step. The featureenhanced image samples after dynamic statistical filtering are input into the DCNN to identify the fault type. In order to verify the effectiveness of the proposed method, take three kinds of faults inclnding unbalance, misalignment and looseness of rotating machinery as objects, the fault type identification was carried out. The analysis results show that the proposed method can effectively identify the fault type. Compared with other methods such as ANN and CNN, the proposed method has better recognition accuracy.