The invention discloses an analog circuit fault diagnosis method based on depth learning and complex characteristics. A fault-free state and all fault states are simulated by using simulation software; different representative working frequency points are set successively; an amplitude and phase of a fault-free signal are measured at each measuring point and a real value and an imaginary value of the signal are obtained by calculation; the real values and the imaginary values are processed to form sample vectors; and tag marking is carried out according to a fault state. A classification network is constructed by using a self-coding network and a classifier; training is carried out by using the sample vectors and the corresponding tags; when a fault diagnosis needs to be carried out on the analog circuit, different representative working frequency points are set successively; current amplitudes and phase are measured at all measuring points; sample vectors are constructed according to a pattern; the sample vectors are inputted into the trained classification network to obtain a classification result, thereby obtaining a fault diagnosis result. According to the analog circuit provided by the invention, on the basis of combination of the self-coding network with complex characteristics of signals, the accuracy of analog circuit fault diagnosis is improved.