Qun You, Shaoqi Zhang, Xiaoyu Zou, Jinhao Chen, Yizhuo Zhang
Graph convolutional networks (GCNs) have become a mainstream approach for skeleton-based action recognition by modeling spatial dependencies among human joints. However, existing methods mostly rely on learnable adjacency matrices or average relations over the entire action sequence to construct graph structures. Such designs mainly represent the overall static structure of the skeleton and may be insufficient for characterizing dynamic changes in joint relations during action execution. To address this problem, this paper proposes a graph convolutional network with joint relation topology evolution modeling, termed JRTE-GCN. Specifically, the proposed method first constructs a topology evolution matrix from frame-level joint distance sequences by calculating the temporal standard deviation and the difference between the initial and final stages of an action. This matrix reflects both the fluctuation intensity and stage-wise variation of joint relations. Subsequently, persistent homology is used to extract structural features from the matrix, thereby capturing the dynamic evolution of joint relations. Finally, the extracted evolutionary features are transformed into layer-wise biases and fused with the average topological bias through a learnable residual mechanism. The fused biases are incorporated into the graph convolutional feature extraction process, enabling joint modeling of the overall skeleton structure and dynamic relational changes. Experimental results on NTU RGB+D 60, NTU RGB+D 120, and Northwestern-UCLA show that JRTE-GCN achieves competitive recognition accuracy under multiple evaluation protocols, demonstrating the effectiveness of the proposed method.