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Due to the complexity, nonlinearity and tolerance of analog circuit fault model, the feature extraction of analog circuit is difficult and rely heavily on the expert’s experience. In order to solve this situation, this paper tried to find a method of the optimal analog circuit fault feature extraction based on the wavelet analysis.The Experimental circuit is four op-amp biquad high-pass filter circuit. The fault feature was extracted from the voltage data by using both Morlet and Haar wavelet with multiple perspectives, then comparing the effect of feature extraction with the diagnostic results of the Clonal Selection Algorithm. Results shows that the best fault diagnostic rate is closed to 89% in different circumstances of two kinds of wavelet, which prove that both two method are available and useful. And results shows the effectiveness of the single point sampling data, at the same time, the level of details of fault feature is positively related to the accuracy of diagnostic. These have the guiding significance for the fault feature extraction in the practical large-scale analog circuit.