Aiming at the problem of accurate diagnosis of ball milling load state under complex grinding conditions， a load state diagnosis method of ball milling based on Deep Wide Residual Shrinkage Networks （DWRSNs） is proposed. Firstly， a wide-convolutional neural network is used to extract the short-term features of the vibration signal， a three-layer deep residual shrinkage network is established， and a soft threshold function is used for nonlinear transformation. Then， the advanced features of the load state oriented are extracted based on the self-learning threshold of the attention mechanism module. And discrimination of the load state of the ball milling is realized through the full-connection layer and the soft layer. The measured results prove that the DWRSNs method proposed in this paper is superior to the existing DCNN， ResNets， and DRSNs diagnostic methods in terms of fit， convergence speed， and learning ability. Meanwhile， the exacted vibration signal features are highly representative， the compactness within the cluster is high， and the boundary between the clusters is obvious after TSNE visualization. The accuracy of the DWRSNs diagnostic test set of the proposed method exceeds 99%， and the cross-entropy loss is 0.0772. Compared with the existing load state diagnosis method， it has higher accuracy and less time-consuming diagnosis and can achieve accurate identification of the load state of the ball milling and provide an effective and reliable criterion for optimizing the control of the process of beneficiation and grinding and improving the efficiency of grinding.