To achieve fault diagnosis of gas-insulated switchgear (GIS) and improve diagnostic accuracy, this paper proposed a GIS discharge fault diagnosis method based on wavelet packet singular spectrum entropy and whale optimization algorithm optimized support vector machine (WOA-SVM). First, the wavelet packet singular spectrum entropy of the ultra-high frequency signals during GIS discharge was extracted as feature vectors. Then, WOA was used to find the optimal parameters for SVM, establishing an accurate classification model. Finally, experiments simulating typical GIS discharge faults were conducted, and three algorithms-SVM with grid search parameters, SVM with particle swarm optimization, and the proposed WOA\|SVM\|were applied to identify GIS discharge fault types. The results showed that the proposed WOA-SVM algorithm achieved higher fault identification accuracy, better fitness, and faster convergence.