Gas insulated metal-enclosed switchgear (GIS) partial discharge pattern recognition is an important part of the insulation fault diagnosis and state evaluation. To achieve accurate identification of discharge types, a method based on particle swarm optimization deep belief network (DBN) was proposed. The weight parameters of DBN network were optimized by particle swarm optimization (PSO) algorithm to improve the learning ability of the network for partial discharge characteristics. Firstly, a sample set of GIS monitoring data of four types of partial discharge was selected to analyze the proposed method. Secondly, the improved PSO algorithm combined with the sample data was used to determine the initial optimal weight parameters of the DBN network and establish the initial DBN network. Then, the partial discharge recognition model was obtained by training the initial DBN network with training samples. Finally, based on the partial discharge data of GIS equipment of offshore power platform in Bohai oilfield, a variety of different partial discharge identification models were used to analyze the data samples. The results show that the proposed PSO-DBN model can effectively identify the type of partial discharge of GIS equipment, and has a higher accurate recognition rate than the traditional DBN network, back propagation(BP), support vector machine (SVM) and convolutional neural network (CNN).