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Aiming at the problem of low control accuracy of traditional systems, a surgical robot control system design based on convolutional neural networks is proposed. According to the control principle of surgical robot based on convolutional neural network, the overall structure of the control system is designed. The CAN adapter card is directly inserted into the PCI slot as the core component of the host computer. The Lib library and DLL library written in C ++ are used to provide suitable driver programs. With card. Through the three nodes of the lower computer, the relevant signals are processed, and the range conversion and limit violation judgment are performed to ensure that the robot will not lose control. The 80C592 microcontroller is selected to design the joint drive node structure, and the controller is provided with a differential transmission and reception capability to the bus in a high-speed operation mode to avoid external interference. The vision-based lens-holding arm is designed to provide a surgical field of vision for up, down, left and right, and back and forth movements during the operation. FN3002 force sensor and MPS-M pull-type displacement sensor were used to obtain relevant sensing data. Convolutional neural network deep learning method was used to design the lens arm motion control steps. VC ++ 6.0 tools were used to control software programs to avoid jitter or The appearance of mishandling the main hand. From the experimental results, it can be seen that the trajectory planning of the mirror holding arm of the system is consistent with the expected trajectory, which simplifies the complexity of the control system.