Aiming at the deficiency of current feature extraction methods of analog circuit incipient fault diagnosis, the feature extraction method applying deep belief network (DBN) technology is presented. Chaos particle swarm optimization (CPSO) algorithm is employed to optimize the learning rates of the restricted Boltzmann machines in DBN and further improve the feature extraction performance. Compared with other commonly used feature extraction methods, the proposed DBN feature extraction method can extract the deep and essential features of incipient faults. The proposed method also has the features, such as the same high fault aggregation degree and obvious different fault separation capacity. Twostage fouropamp biquad lowpass filter simulation circuit and SallenKey bandpass filter circuit board were used to carried out incipient fault diagnosis experiments, and the obtained fault diagnosis accuracies are 9813% and 100%, respectively.