Abstract: Aiming at the problem of complex operating environment and interference between different parts in the industrial production of robotic arm grasping, this paper proposes a target recognition and grasping method based on deep learning, so as to reduce the uncertainty of object position in the grasping scene and improve the success rate of detection and grasping. The Convolutional Block Attention Module (CBAM) is used to improve YOLO-V5 to enhance the attention and extraction ability of convolutional networks on image features and improve the detection accuracy. The average recognition rate of the improved network increased by 5.26%, proving that the improvement was effective and successful. In this paper, a set of robotic arm grasping system is built by AUBO-i5 manipulator, electric gripper, camera, and six-axis force sensor, and the experimental results show that the proposed method can adapt to different grasping scenarios in real grasping, reduce external interference, improve the success rate of grasping, and have good application prospects.