In view of the existing problem of complex electromechanical equipment fault diagnosis,such as large amount of data, the fault feature extraction difficulties, combining with strong perception and selflearning ability of the theory of deep learning, a complex electromechanical equipment fault diagnosis method based on deep belief networks and multi information fusion was proposed. The original time domain signal data were input into the deep belief network to train, and the whole network was adjusted by reverse trimming to improve the classification accuracy. At the same time, Batch Normalization and ReLu activation function were added to the training process to reduce the chance of over fitting and improve the convergence speed of the network. This method was applied to the fault diagnosis of tool of the complex numerically controlled production center.The results show that this method is more accurate than the traditional BP neural network algorithm and deep neural network algorithm using Sigmoid activation function.