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The accuracy of soft fault diagnosis for analog circuit with tolerance is relative low, therefore, a new method based on AdaBoost and GABP is proposed. Firstly, fault modes are simulated by Monte-Carlo method, furthermore, the effective point extraction method is used to extract the characteristic of the fault-pattern, on this basis, original samples of neural network is constructed using the normalized fault data. Secondly, GA algorithm and the L-M algorithm are used to optimize BP neural network to construct GABP classifier. Finally, the GABP network was boost by the AdaBoost algorithm to construct the AdaBoost-GABP combination classifier. The example shows that, the method proposed has higher accuracy and lower error than the traditional single classifier, beyond that, the method overcomes the defect that it is easy to fall into local optimum for the single classifier.