To improve the optimization planning and design capabilities of indoor spaces, a small area indoor en- closed space segmentation method based on point cloud data semantic segmentation under combined light perspective is proposed. Construct a three-dimensional environmental information perception model for small indoor enclosed spaces, extract coordinate information of indoor enclosed space images using indoor space point clouds, and map the fused spa- tial information to the high-resolution spatial heterogeneous unit structure using semantic combination feature segmenta- tion method. Introduce subspace projection feature information with constraints, and combine the high-resolution seg- mentation image model parameter fusion method with combined light perspective, Extract small indoor enclosed end el- ements and use point cloud data semantic segmentation method to achieve spatial segmentation. The simulation results show that this method can effectively realize the Iterative reconstruction of complex indoor scenes. The Root-mean- square deviation of spatial segmentation is low, the maximum is 0. 808%, the peak signal to noise ratio is high, the maximum is 42. 156 dB, and the spatial segmentation speed is fast, the average is 12. 83 ms.