Three-dimensional (3D) target recognition plays a critical role in autonomous robot grasping.The original point-pair feature-based 3D target recognition methods are facing the challenges,such as slow recognition speed and poor recognition accuracy in severely occluded scenes.To address these issues,this paper proposes a 3D target recognition method based on the point-pair feature of curvature key points.First,the key points are extracted based on the curvature,which is estimated by mean angle of neighbored point cloud normal vector in a point cloud.Second,the point-pair features are established for key points to eliminate redundant point pairs in the point-pair feature Hash table.Third,through processes of pose clustering and hypothesis verification,the pose optimization is achieved.In particular,we optimize the candidate poses by pose clustering and then refine the pose by the iterative closest point (ICP) algorithm.Finally,the incorrect poses are eliminated by the hypothesis verification of poses according to the overlap score. The hypothesis of optimal posture is obtained. The experiment results show that the proposed method achieves an average recognition accuracy of 95.2% on a public dataset,and significantly reduce the construction time of the point-pair feature Hash table,while enhancing the recognition performance in severely occluded scenes.