The partial discharge fault identification method based on the fusion of multi-dimensional information sources can greatly improve the accuracy and fault tolerance in the fault identification of power equipment. In this paper,four typical partial discharge models,namely corona discharge,suspended discharge,floating discharge and air-gap discharge are prepared. The partial discharge signals generated by different discharge models are collected by ultrasonic (Ultra),very-ultra high frequency (V-UHF) and pulse current method (PCM) sensors. Firstly,the deep convolutional neural network (CNN) algorithm is used to train the measurement data of different sensors,and then the Dempster-Shafer (D-S) evidence theory is used to perform fusion calculation on the recognition results of multi-dimensional information sources. Finally,according to the fusion calculation results,the identification conclusion is made. The results show that the fault identification model based on multi-dimensional information sources constructed in this paper has higher accuracy than that based on single information source. When a misjudgment occurs in one of the multi-dimensional information sources,the model can still correctly identify the type of discharge,which indicates that the model has better fault tolerance for the information sources and the recognition effect is good.