The core of point cloud registration is to estimate the transformation matrix. There is partial overlap， high noise， and density difference between two point cloud pairs. The existing methods cannot accurately solve the problem of feature alignment between significant point cloud correspondences. Therefore， a significance peak and feature alignment network （SPFANet） is proposed to achieve point cloud registration from coarse to fine one. SPFANet consists of three parts： multi-significance peak detector， coarse registration， and fine registration. Firstly， the multi-significance peak detector introduces a re-weighted peak loss method based on descriptor variance and overlap score to remove non discriminatory and non overlapping key point clouds. Secondly， the coarse registration stage detects the complementary key point sets to compute the coarse registration scheme. Finally， the fine registration stage introduces a feature metric framework with a forward-backward transform to refine the coarse registration scheme and achieve efficient point cloud registration. The effectiveness of SPFANet is validated through experiments on the same source 3DMatch dataset and cross-source 3DCSR dataset.