This paper presents a fault feature recognition method based on factor analysis techniques for wavelet feature extraction in LS-SVM algorithm existing problem of wavelet bases function selection,wavelet decomposition level and coefficient selection. The method computes factor loadings and factor scores by constructing a correlation matrix of sample data,extract factors 1-3 to compose feature vector automatically according to the cumulative contribution rate,thereby reduce the dimension of the input,improve the efficiency of training and diagnostic algorithm,reduce the convergence difficulty. The simulation results of four op-amp biquad high-pass filter show:The diagnostic accuracy of the algorithm in this paper is beyond similar methods,while increasing the training time and the efficiency of diagnosis. 