Aiming at the problems of large computing resource consumption and lack of real label data in the process of partial discharge mode intelligent identification in GIS. This paper uses the MobileNet-V2 model whose activation function is Leaky ReLU to extract a large amount of image feature information while reducing the amount of model parameters.It also integrates migration learning to pre-train the model parameters, which reduces the network's need for input data and improves the recognition accuracy of the model.The results show that: the parameter quantity of the model in this paper can be reduced to 2.24×106, and the average accuracy of interference and partial discharge pattern recognition in GIS reaches 95.8% and 92.1%, respectively.Compared with the traditional deep learning model, this model can significantly reduce the computational complexity and improve the accuracy of pattern recognition, which has certain value and significance for effective, intelligent and lightweight operation and maintenance of actual GIS equipment.