An under-sampled non-uniform density multi-station 3D point cloud alignment method based on manifold clustering is proposed, to address the problem that the point cloud data from each view overlap with each other, and the uneven point cloud density caused by different overlapping areas directly affect the multi-station cloud alignment accuracy. First, the geodesic distance is used as a similarity measure to cluster the unbalanced point cloud data to achieve a streamlined point cloud data. Then, the K nearest neighbour (KNN) method is used to calculate the number of points within the radius of each point, and the point cloud is divided into denser and less dense point clouds. Next, the denser regions are clustered and the surfaces are fitted to each cluster, and the curvatures of all points on the surfaces are calculated. The points with greater curvature are extracted, so that the denser regions and the points with greater curvature are extracted so that the number of point clouds in the denser regions and the less dense regions are balanced, resulting in more balanced point cloud data. Finally, the point clouds are undersampled using manifold clustering and clustered using K-means clustering, which updates the clustering centres and the rigid transformation matrix to achieve non-uniform density multi-station cloud alignment. Compared with the random sampling method and the uniform sampling method, the proposed method has a smaller chamfer distance and preserves the local feature information of the point cloud. The experiments on the Bunny dataset in the Stanford University public dataset indicate that the proposed method improves the alignment efficiency by more than 60% while ensuring the accuracy of the alignment.