Parallel mechanism has the advantages of high bearing capacity, high precision and high stiffness, and is widely used in all walks of life in the industrial field. In order to reduce the difficulty of measuring and compensating the mechanical errors of parallel mechanisms and realize the accurate control of the end of the mechanism, a method of end error compensation was proposed based on the combination of Jacobian and RBF neural networks. Taking a 3-PTT parallel mechanism as the research object, the forward and inverse kinematics of the mechanism were analyzed using geometric method, and the correctness of the mathematical model was verified by Matlab/GUI. Jacobian was solved according to kinematics model, and constraint singularity and motion singularity of mechanism are analyzed. In order to verify the effectiveness of the mechanism end error compensation method, two experimental conditions were set up, namely, whether there was a return error compensation of the lead screw and whether the end was subjected to different loads, and the end position was measured by the laser tracker. By training the compensation model through the collected data, the error compensation is completed. The experimental results show that the axial (x axis) and radial (y axis) position errors of the end of the mechanism are reduced by more than 90%, and the vertical (z axis) position errors are reduced by more than 80% after using the error compensation method. In this paper, the error compensation effect is good, the precision of the end of the mechanism is obviously improved, and the proposed method is effective.