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In this paper, we propose a novel two-level hierarchical framework for three-dimensional (3D) skeleton-based action recognition, in order to tackle the challenges of high intra-class variance, movement speed variability and high computational costs of action recognition. In the first level, a new part-based clustering module is proposed. In this module, we introduce a part-based five-dimensional (5D) feature vector to explore the most relevant joints of body parts in each action sequence, upon which action sequences are automatically clustered and the high intra-class variance is mitigated. In the second level, there are two modules, motion feature extraction and action graphs. In the module of motion feature extraction, we utilize the cluster-relevant joints only and present a new statistical principle to decide the time scale of motion features, to reduce computational costs and adapt to variable movement speed. In the …