Point cloud registration is one of the key technologies for 3D reconstruction. To address the problems of the iterative closest point algorithm (ICP) in point cloud matching, which requires high initial position and low speed, a point cloud registration method based on adaptive local neighborhood feature point extraction and matching was proposed. Firstly, according to the relationship between the local surface change factor and the average change factor, feature points were adaptively extracted. Then, the fast point feature histogram (FPFH) was used to comprehensively describe the local information of each feature point, the coarse alignment was achieved combining with the random sampling consistency (RANSAC) algorithm. Finally, according to the obtained initial transformation and feature point based ICP algorithm, the fine alignment was achieved. The alignment experiments were conducted on the Stanford dataset, noisy point cloud and scene point cloud. The experimental results demonstrate that the proposed feature point extraction algorithm can effectively extract the features of the point cloud, and by comparing with other feature point detection methods, the proposed method has higher alignment accuracy and alignment speed in coarse alignment with better noise immunity; compared with the ICP algorithm, the registration speed of the feature point based-ICP algorithm in the Stanford data set and scene point cloud is increased by about 10 times. In the noisy point cloud, the registration can be performed efficiently according to the extracted feature points. This research has certain guiding significance for improving the efficiency of target matching in 3D reconstruction and target recognition.