Addressing the issues of uniform point distribution, severe surface feature homogenization, and low fine-grained classification efficiency in standard polyhedron point cloud data, this paper proposes a fast polyhedron point cloud classification method that combines K-means clustering and neural networks. First, taking the standard polyhedron point cloud dataset as the research object, we adopt the K-means clustering algorithm to preprocess unordered 3D point cloud data. The surfaces of the target polyhedrons are rapidly segmented based on the spatial coordinates and geometric distance features of the point cloud, which effectively eliminates redundant point cloud data and concentrates feature extraction on the core surface features of the polyhedrons. This method resolves the problems of redundant feature extraction and long computation time that plague traditional point cloud classification methods for regular geometric objects. On this basis, the structured feature data obtained after K-means clustering is input into a neural network model for iterative training and feature learning. This process fully explores the surface topological features and spatial distribution rules of standard polyhedron point clouds, enabling accurate classification and recognition of polyhedron point clouds. Experimental results show that the proposed hybrid method integrates the advantages of efficient preprocessing from traditional clustering algorithms and high-precision feature learning from neural networks. Compared with single-model neural network classification methods, it greatly improves the computation speed and recognition accuracy of surface classification for standard polyhedron point clouds. It can provide effective technical support for rapid analysis, 3D reconstruction, and geometric feature detection of regular 3D geometric point clouds.