Aiming at the problems of large deviation and high difficulty in trajectory control of hybrid manipulator, a faulttolerant algorithm based on depth learning was proposed. In Cartesian space, the correlation between joint vectors was determined based on nonlinear function, and the convergence performance of the trajectory faulttolerant correction was improved; the dynamic position movement information of the hybrid manipulator was trained based on DBNs model, and the optimal solution was searched in the global scope; the stability and applicability of the closedloop operation of the manipulator system were guaranteed according to the trajectory characteristics of each joint space and the depth learning faulttolerant mechanism. The simulation results show that the deviation between the actual movement curve and the expected curve of the six joints is small, and the complexity of the algorithm is lower.