Aiming at the problems of nonlinearity and nonstationarity of analog circuits, and the ambiguity and randomness of diagnostics caused by the tolerance of circuit components, an analog circuit fault diagnosis method combining local mean decomposition (LMD) cloud model feature extraction and ABCLSSVM classifier is proposed. First, the LMD algorithm is used to decompose the initial fault signal, and the cloud digital feature of the decomposed signal is calculated by the cloud inverse generator, and the obtained cloud digital feature is constructed as a fault feature vector. Then, a part of the fault feature vector is input as a test sample into the LSSVM optimized by the artificial bee colony (ABC) algorithm, and the circuit faults are classified and identified to obtain the classification accuracy of each fault. Two international benchmark circuits, CTSV and Sallen_Key, are used as verification objects. The results show that the fault features extracted by this method can well reflect the fault status information of the circuit, and the fault diagnosis accuracy of the proposed method reaches 99%.