The invention discloses a trajectory recovery method based on depth learning and Kalman filter correction, which comprises the following steps: S1, discretization of trajectory points; S2,loop NeuralNetwork and Trajectory Modeling; S3, trajectory recovery; S4, using spatio-temporal attention mechanism, obtains attention model based on sequence-to-sequence model; S5, combines Kalman filter and circulating neural network, introduces Kalman filter to optimize the mean square error, cooperatively trains the Kalman filter and the attention model, and obtains the final model. The invention providesa trajectory recovery method based on depth learning and Kalman filter correction, the transfer law between the trajectory points is modeled by the loop neural network. The attention mechanism in depth learning is used to help trajectory recovery. Finally, Kalman filter is introduced to model the movement of the object in time and space, which reduces the unexplainability and error of depth learning model, has stronger explainability, and reduces the error of trajectory recovery.