Due to the limitations of the application range and the defects of their own structures, the traditional observers such as state observers and Kalman filters may complicate the design of motor control systems, reduce the observation accuracy and limit the observation range, etc. Deep learning has the advantages of nonlinear fitting capability, good generalization ability and strong feature extraction ability, etc. Deep learning observer is taken as the main line, the advantages of its application in motor control system is described. The principle and structure of deep learning with four typical networks is taken as examples, the analysis of the application of deep learning in motor control is listed and focused on, and on the future developing direction of deep learning in motor observer control is forecasted.