Action recognition is an important technology in computer vision, which can be categorized into video-based and skeleton-based action recognition according to different input data. The 3D skeleton data avoids the influence of illumination, occlusion, and other factors, yielding more accurate action descriptions. Now, human action recognition based on 3D skeleton has been paid more attention. Methods for human action recognition based on a 3D skeleton can be divided into the end-to-end black-box method and the pattern recognition-based white-box method. The black-box method in deep learning involves large parameters and can learn classification knowledge from a large amount of data. However, deep learning is difficult to explain and can only provide an overall recognition result. Compared with the black-box method, the white-box method has an explainable recognition process and an adjustable algorithm. Nevertheless, some white-box methods only focus on algorithmic improvements, using formulas to represent and classify actions, without reflecting the difference and connection among actions. Therefore, this study designs a white-box method with a visible classification process. This method uses a tree structure to organize action data hierarchically, constructing an individual classification hierarchy according to the differences between the same actions and an action classification hierarchy according to the discrepancies among different actions. Various measurement algorithms are also incorporated into the system. This study selects the nearest neighbor and dynamic time warping algorithms for experiments. The advantage of a hierarchical structure is that a variety of knowledge can be implanted to it according to various requirements so that actions can be classified from different perspectives. In the experiments, key posture knowledge and human body structure knowledge are implanted into the hierarchy structure. With the implantation of knowledge, the hierarchy structure dynamically changes.