Aiming at the problem that point clouds are prone to misregistration under multiple interference conditions of noise, occlusion and similar features, a point cloud registration method based on multifeature key point extraction algorithm and adaptive scale fusion features was proposed. In keypoint extraction, multiple features are computed simultaneously to make keypoints more descriptive and robust. On the basis of adaptive scale, FPFH and RoPs features are used for initial registration and error point pair elimination, and multiple similar transformation matrices are obtained respectively. After the above solution is completed, the matrix obtained by the two is formed into a set for clustering, and the class with the largest number of matrices is averaged as the final result to complete the feature fusion. The experimental results show that the RMSE, ATI and ERR of the proposed algorithm are 046 mm, 1 and 037 in the actual scenario when a few unrealized error registration points are ignored. The accuracy of the dataset test was 99.3%. It shows that the algorithm has high accuracy and robustness.