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In order to ensure the grasping accuracy of the manipulator and the stability of object grasping, this paper designs a manipulator grasping control system based on convolutional neural network. In the hardware part of the system, image, position and pressure sensors are added, the manipulator grasping controller and motion driver are modified, and the image sensor equipment is used to obtain the manipulator grasping target image that meets the quality requirements, providing hardware support for the manipulator grasping control function. In the software part, the convolution neural network algorithm is used to extract the image features and determine the position of the target grasped by the manipulator. Based on the detection results of the current position of the manipulator, the grasping route of the manipulator is planned, and the grasping angle and grasping force of the manipulator are estimated. Finally, through the calculation of the manipulator grasping parameter control quantity, the manipulator grasping control function of the system is realized under the support of the controller. The experimental results show that the average values of position control error and speed control error are 0.192 m and 0.138 m/s respectively, and the probability of object grasping and falling is significantly reduced.