Abstract: To solve the difficult diagnosis problem caused by the nonlinearity and high dimensionality of the analog circuit output signal, the support vector machine（SVM） classifier parameter optimization algorithm was proposed for analog circuit fault diagnosis. The fault features were extracted from the circuit output signal by the combination S-GLCM of S-transform and gray-level co-generation matrix（GLCM） method. The particle swarm algorithm（PSO） fused with particle filtering algorithm（PF） was used to update the position and velocity of particles in real time by resampling to efficiently optimize the SVM parameters, and the feature vectors were brought into the model to conduct the training and testing and complete the high-precision fault diagnosis of each fault mode of the circuit. The reliability of the method was analyzed by two international benchmark circuit experiments. The results show that S-GLCM has great advantages in the processing of nonlinear and non-stationary signals, and each group of 1 500 sampling points of the circuit output signal can be reduced to 8-dimensional feature vector with reduced redundant information. Compared with the unoptimized algorithm, the diagnostic accuracy of the proposed method is improved by about 11.2%.