Partial discharge is closely related to the insulation state of power equipment. Accurate identification of partial discharge types is of great significance to ensure the operation of the power grid. A partial discharge pattern recognition method based on deep learning and multi-model fusion is proposed in this paper. Firstly,four kinds of typical partial discharge defect models in the switchgear are designed and built,and the phase resolved partial discharge (PRPD) spectrum is collected to estalish sample set. Then,a deep residual network based on transfer learning is built to identify partial discharge defects. Finally,the deep residual network (DRN) model is fused with the traditional recognition model by Sugeno fuzzy integral. The experimental results show that the transfer learning model has better update ability and generalization performance than the non-transfer learning model does,and the fusion model has higher recognition accuracy than the single model does. The proposed method can accurately identify the types of partial discharge defects,and has a certain reference value for the operation and maintenance of power equipment.