Fault diagnostic methods based on deep learning for industrial processes are becoming a research hotspot. Most existing methods focus on algorithmic improvements and attempt to establish a single model to extract effective features of faults. However, effective information related to different faults is diverse. Therefore, instead of using a single model to extract features and build a model to correctly diagnose all types of faults, we propose a novel fault diagnostic method based on deep learning and multimodel feature fusion. First, the minimum redundancy-maximum relevance method is used to select the variables that are the most relevant to each fault. Next, the features of each fault are extracted using a stack autoencoder, and the corresponding residual matrices are obtained. The features and residuals obtained using each model are then spliced as new inputs to establish a classifier for fault diagnosis. Finally, we apply the proposed method to the Tennessee Eastman benchmark process to demonstrate its performance and efficiency.