To address the challenges in solar greenhouses where cucumber canopy point clouds are highly dense, leaves exhibit severe overlap, and inter-plant adhesion is pronounced, resulting in insufficient accuracy and poor robustness of traditional point cloud segmentation algorithms, this study proposes a comprehensive cucumber hierarchical segmentation framework (Cuc-HiSeg) for terrestrial laser scanning (TLS) point clouds, enabling high-precision segmentation from population level to individual plants and further to leaves. High-resolution three-dimensional (3D) point cloud data of cucumber canopies at the seedling, early flowering, and fruiting stages were collected using TLS. For segmentation from population to individual plants, an improved hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm integrating k-dimensional tree (KD-tree) optimization and secondary clustering with mean shift was developed, improving the F1-score by 0.7%, 5.0%, and 8.0% across the three growth stages compared with the original algorithm. For leaf segmentation, a leaf point cloud data augmentation algorithm named CuLeafExpander was proposed to enhance the generalization capability of deep learning models. Experimental results demonstrate that the improved HDBSCAN and region-growing (RG) algorithms attained F1-scores above 91.9% and 93.9%, respectively. The proposed Cuc-HiSeg provides technical support for efficient processing of crop point clouds and advances digital crop phenotyping and intelligent greenhouse management.