This paper proposes a three-dimensional (3D) circular hole recognition algorithm based on the fusion of point cloud normal and projection to solve the problems of poor extraction accuracy and the incomplete edge point extraction of the existing 3D circular hole recognition algorithms. Firstly, k-dimensional tree(KD-tree) assists in establishing the spatial topological relationship of the point cloud. Secondly, K-NearestNeighbor(KNN) is used to search the k neighborhood points closest to the point. The point greater than the threshold is determined as the boundary point by defining the distance threshold. Finally, the fusion of point cloud normal and projection are combined to realize the distinction between feature points and noise points at the edge of the point cloud, and the 3D circular hole features of the point cloud data are extracted. The experimental results show that the algorithm can effectively realize point cloud edge extraction and 3D circular hole recognition.