Aiming at the problems of feature extraction and long model training time in analog circuit fault diagnosis, an analog circuit fault diagnosis algorithm based on deep extreme learning machine is adopted. The algorithm introduces the idea of auto encoder in deep learning into the extreme learning machine to construct a depth network, and forms more abstract advanced features from the underlying fault features. It can learn data features autonomously, avoiding additional feature extraction and selection. Finally, two analog circuits of Sallen-Key and four operational amplifiers with double quadratic high-pass filtering are taken as examples to carry out the simulation research. The experimental results verify the feasibility of fault diagnosis in analog circuits, and also show that the model has fast learning speed, good generalization ability and strong diagnosis ability. The classification accuracy of fault diagnosis can reach 100%, and the diagnosis time is about 0.3 s.