With the increasing demand for processing point cloud data of aircraft blades, existing algorithms face challenges of long registration times and insufficient accuracy when registering large-scale point clouds. In response to this situation, this paper proposes a coarse registration algorithm that constructs feature vectors of point clouds. First, the source and target point clouds are decentralization, and feature points are identified by traversing the Euclidean distance between the decentralization point cloud and the origin point. Based on these feature points, feature vectors for both point clouds are constructed separately. Then, the rotation axis is determined according to the two feature vectors, and the rotation matrix is calculated using the Rodrigues formula to align the two feature vectors. Subsequently, the rotation matrix is calculated with the feature vectors of target point clouds as the rotation axis to ensure the alignment of the feature points. Finally, the source point cloud is aligned with the target point cloud through translation. Through two sets of experiments, the proposed algorithm is compared with three classical algorithms. Results demonstrate that the proposed algorithm has higher accuracy and time efficiency when dealing with medium-scale point cloud data. In the rough registration stage, compare to the comparison algorithms, the registration time of the proposed algorithm is reduced by more than 80%, maintained the registration accuracy of 0.1 mm level, and preserved good precision.