Abstract: In order to improve the speed and accuracy of 3D point cloud target recognition in complex scenes, a 3D target recognition algorithm based on the features of the pairs of key points was proposed. Firstly, point-pair features were established for key points to avoid the feature calculation of local surfaces in the surrounding neighborhood, to reduce space dimensionality and to improve calculation speed. Then a hash table was taken as storage to reduce the time of feature matching, a fast voting scheme was utilized to match and identify the model point cloud and the scene point cloud for the generation of candidate position and pose, and a greedy algorithm was used to cluster and filter the positions and poses. And an ICP algorithm was adopted to optimize the object positions and poses, according to the overlapping rate of registered point clouds to evaluate the point cloud recognition. Finally, some experiments were carried out based on data sets and real scenarios to validate the proposed algorithm. The results show that the proposed recognition method can present better feasibility, effectiveness and robustness to noise, possessing certain practical engineering application value.