Accurate and efficient semantic description of human posture is integral to human behavior recognition. It is also a key to quickly understanding individual states and events. In recent years, human key point detection technology has gained significant development. However, the research on the semantic description of the human pose has not attracted enough attention. To this end, we propose a geometric statistics-based semantic description method for human posture. Firstly, the obtained human key points are divided into several sets. Then, the geometric distribution characteristics of each key point set are calculated to describe the human posture. Finally, the semantics of the human pose is judged using a hierarchical strategy. This method employs the idea of the set to improve the robustness of recognizing human posture. The experimental results on multiple real scene datasets show that the proposed method attains an average accuracy of 90. 8% and 77. 1% for identifying human pose on the IFD and PASCAL datasets for simple and complex singleperson pose, respectively, and 77. 2% on the MPII dataset for the complicated multi-person pose, which are better than the performance of compared approaches. In conclusion, the proposed method can achieve more accurate human pose semantic descriptions despite the absence of some key points.