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In the fault diagnosis of analog circuits, the extraction of fault features is a very important link, and the result of extraction has a direct impact on the final correctness of fault diagnosis.Because of the limitation of single fault feature extraction and to extract the fault characteristics more fully ,a method of fault feature extraction based on wavelet packet analysis and principal component analysis (PCA) is proposed, and three different feature vector fusion models are proposed. In order to obtain the optimal wavelet feature, the characteristic deviation degree is proposed, and the optimal wavelet basis is selected as the standard. Finally, an improved neural network classifier model is constructed, and the results of the fusion are sent into it to verify the results. The specific algorithm and simulation examples are given in this paper, The results show that the proposed method can effectively improve the correctness of fault diagnosis compared with single fault feature extraction method, and when the fusion factor is moderate, the correctness of diagnosis is the highest.