针对双人交互行为识别网络中存在忽略人体间的非自然连接关系和交互关系等突出问题，提出一种改进时空图卷积模型的双人交互行为识别算法。首先通过边卷积操作汇聚节点的边特征，以捕获人体的非自然连接关系；其次利用改进的关系网络，构建了双人之间的交互关系图；然后将边卷积操作分支及交互关系图嵌入时空图卷积网络块，分别构建为边-图卷积块和交互关系块；最后将两者高效融合，提出一个能同时捕捉非自然连接关系和交互关系的改进时空图卷积算法，从而实现双人交互行为识别。为验证网络的有效性，在国际公开大型标准数据集NTU RGB + D上进行测试。实验结果显示，该算法识别准确率达97.77%，相比于基线时空图卷积模型提升了4.28个百分点，提高了双人交互行为特征的表现力，取得了比现有先进网络模型更好的识别效果。

Aiming at the prominent problems of ignoring the unnatural connection relationship and interaction relationship between human bodies in two-person interaction recognition algorithm, a two-person interaction recognition network based on improved spatial temporal graph convolutional model was proposed. Firstly, the edge features of joint point data were aggregated by edge convolution to capture the unnatural connectivity relations inherent in the human body. Secondly, the interaction relationship graph between two people was constructed by using the improved relationship network. Furthermore, the branch of edge convolution and the interaction relationship graph were embedded into the spatial temporal graph convolutional network block, which were constructed as an edge-graph convolutional block and interaction relation graph convolutional block. Finally, an improved spatial temporal graph convolution algorithm was proposed to capture both the unnatural connection relationship and the interaction relationship, so as to realized the recognition of two-person interaction behavior. To verify the effectiveness of the network, it was tested on the international public large-scale standard dataset NTU RGB + D. The experimental results show that the network obtain a recognition accuracy of 97.77%, which is an improvement of 4.28 percentage points compared to the baseline spatial temporal graph convolutional network. It improves the expressiveness of two-person interaction behavioral features, and achieves a better recognition effect than the existing state-of-the-art network models.