Aiming at the problem that it is difficult to apply the fault diagnosis model based on deep neural network in the industrial environment with limited resources, a bearing fault diagnosis method based on compressed deep neural network was proposed. Firstly, the filter corresponding to the output low-rank feature graph in the convolution layer is removed by structural pruning. Then unstructured pruning was used to remove non-important connections in the whole connection layer. Finally, the number of bits required for parameter representation is reduced by quantizing the parameters of the weight matrix, and the storage of parameters is further reduced by using the compression storage method of the weight matrix. Experimental results show that the proposed compression method can greatly reduce the parameter storage and floating point computation of the network, shorten the training time of the network and speed up the response of the network on the premise of high diagnostic accuracy, which provides a beneficial exploration for the industrial application of the deep neural network method.