The skeletonization of three-dimensional point cloud models is an important research area in computer graphics used to describe the topological structure of objects. In the automation measurement and machining of complex cavities, a well-constructed skeleton plays a crucial role in generating automated equipment motion trajectories. However, when sampling the point cloud of complex-shaped cavities, limitations such as the sensor’s range and object reflectivity often result in unevenly distributed or missing data in the extracted point cloud model. This greatly impacts the centrality of the generated skeleton and the extent of three-dimensional feature restoration. To address these issues, this paper proposes an algorithm for extracting the main skeleton of complex cavity point clouds. The algorithm first inputs the sensor’s initial parameters based on practical measurement needs and performs segmented scanning and sampling of the point cloud from the starting point. Local normal distribution trends of the sampled segment are then analyzed to ascertain axial features, followed by planar projections using multiple orthogonal bases and calculation of the hollow area. Finally, combining the calculated results of axial features and maximum hollow area, the growth direction and step length of the main skeleton are estimated. The process is repeated until a complete main skeleton is generated. Experimental results show that the proposed algorithm can still accurately restore model features when faced with complex shapes, uneven densities, and local data missing point clouds.