Abstract: In order to improve the accuracy and optimization efficiency of analog circuit fault diagnosis based on support vector machine (SVM), on the basis of gray wolf optimization (GWO) algorithm, this paper proposes a modified classifier that uses the improved gray wolf optimization (IGWO) algorithm to optimize the parameter of SVM (IGSA-SVM) by introducing the nonlinear convergence factor, dynamic weight and boundary variation strategy. Firstly, the fault samples are collected in the Sallen-Key bandpass filter circuit and four opamp biquad highpass filter circuit, and wavelet packet feature extraction is applied to fault samples. Then, feature-extracted samples are divided into training samples and test samples. The IGWO algorithm is used to optimize the penalty parameter C and the kernel parameters γ in SVM to obtain the optimal SVM classifier model. Finally, compared with SVM classifiers optimized by other algorithms, the results show that the IGWO-SVM classifier can prevent the population from falling into a local optimum, and the convergence speed has been greatly improved.