Superpoint matching is a critical step in coarse-to-fine point cloud registration, and its performance directly affects the accuracy of subsequent point matching and pose estimation. However, most existing methods establish correspondences mainly relying on feature similarity, without explicit modeling of spatial structure, which easily leads to unstable matching in complex scenarios such as noise, occlusion, and low overlap. To address these issues, this paper proposes a coarse-to-fine point cloud registration method guided by prior correspondences. First, prior superpoint correspondences are constructed using rigid transformations estimated by existing SOTA methods, and are serially encoded via a prior encoding module to provide explicit constraints for feature learning. Furthermore, multiple geometric information including pairwise distances, angles, and normals is introduced and uniformly encoded to enhance spatial struc-ture representation. On this basis, a prior-guided sparse mixture-of-experts attention mechanism is designed to differentially model features in overlapping and non-overlapping regions, thereby improving feature discriminability and structural consistency. Using the learned features, the model gradually establishes correspondences through superpoint matching and point matching, and estimates the final rigid transformation with RANSAC. Experiments on the 3DMatch dataset show that when sampling 1000 point correspondences, the proposed method achieves an inlier ratio of 80.7% and a registration recall of 92.9%, which are 5.5% and 1.1% higher than the baseline method respectively, verifying the effectiveness of the proposed method in terms of accuracy and robustness.