Semi-supervised learning plays a crucial role in airborne laser scanning (ALS) point cloud semantic segmentation to reduce the cost of sample labeling. However, the prevailing semi-supervised approaches mainly focus on the local and global feature aggregation, which neglects the multi-scale and neighborhood structure properties of ALS point clouds. To address this issue, we propose an innovative approach called the Hybrid Structured Constraint Network (HSCN) for semi-supervised semantic segmentation of ALS point clouds. HSCN makes full use of a large number of unlabeled samples to guide the model training under limited labeled samples. To process the unlabeled samples, we construct a global awareness loss to constrain the global distribution of point clouds. Then, we also design a multi-scale geometric structure similarity metric loss to align the neighborhood structure for point clouds at different scales. In addition, we utilize the multi-scale features to develop a multi-level fused pseudo-label generator for obtaining high-value pseudo labels, and then a pseudo-label loss is constructed to reduce the class mean probability discrepancies. The proposed HSCN fully utilizes the multi-scale and neighborhood structure properties of unlabeled samples to achieve a robust model under limited labeled samples. Extensive experimental analysis using three benchmark datasets (i.e., ISPRS, LASDU, and DFC2019) reveals that our proposed method achieves comparable advantages to some existing advanced fully-supervised approaches, even only 0.1% labeled samples for model training. Code: https://github.com/SC-shendazt/HSCN.