For the issues of low registration accuracy and high mismatch rate in low-overlap point cloud registration due to insufficient descriptive information of keypoint features and minimal overlapping regions， this paper proposed a low-overlap point cloud registration network that integrates position encoding and overlap masks was proposed to reduce mismatch rate and improve registration accuracy. First， a PointNet-based point-wise feature encoder was employed to extract keypoints， which were then enhanced by fusing their feature information， coordinate data， and position encoding to generate more discriminative keypoint descriptors. Second， the fused features were processed through self-attention and cross-attention modules to strengthen the descriptive power of point cloud features and enhance contextual interaction， thereby addressing the problem of insufficient keypoint descriptive information. Third， an overlap mask module was introduced after the attention modules to filter out keypoints from non-overlapping regions through learned masks， further reducing mismatch rate. Finally， optimal matching was achieved using the Sinkhorn algorithm， followed by refinement with the Iterative Closest Point （ICP） algorithm to enhance registration accuracy. Experimental results on the CODD and KITTI datasets， compared with various existing low-overlap point cloud registration methods， demonstrate that the network with ICP refinement performs superiorly. Specifically， on the CODD dataset， it reduces the Relative Translation Error （RTE） and Relative Rotation Error （RRE） by 53.29% and 42.72%， respectively， compared to the state-of-the-art method CoFiI2P （Coarse-to-Fine correspondences for Image-to-Point cloud registration）， while improving the Registration Recall （RR） by 0.2 percentage points. The results indicate that the proposed network effectively extracts descriptive information from keypoint features and significantly improves point cloud registration accuracy in low-overlap scenarios.