The robot faces different shapes and sizes of objects in the task of grasping. The scattered objects in the scene may have different poses and positions, which make the task of recognizing positions and poses of objects more difficult. In view of this, a threedimensional scene recognition method for robotic grasping is proposed. It makes up a defect that the 2D detection method is sensitive to the field of view in robotic grasping task. Firstly, the convolutional neural network is designed to detect the object in the RGB image. Eight corner points of the three-dimensional bounding box of objects, and the center point of the object are generated. Secondly, a method is proposed to calculate the best position and pose for robotic grasping. Finally, the robot is controlled to grasp objects. In real scene, the detection accuracy reaches 88% , and the grasping success rate based on the designed three-dimensional recognition network is up to 94% . In summary, the designed network can effectively find a suitable grasping pose. The grasping success rate is improved. It is able to meet higher requirements.