针对基于人体骨架序列的动作识别存在的特征提取不充分、不全面及识别准确率不高的问题，本文提出了基于多分支特征和多尺度时空特征的动作识别模型。首先，利用多种算法的结合对原始数据进行了特征增强；其次，将多分支的特征输入形式改进为多分支的融合特征信息并分别输入到网络中，经过一定深度的网络模块后融合在一起；然后，构建多尺度的时空卷积模块作为网络的基本模块，用来提取多尺度的时空特征；最后，构建整体网络模型输出动作类别。实验结果表明，在NTU RGB-D 60数据集的两种划分标准Cross-subject和Cross-view上的识别准确率分别为89.6%和95.1%，在NTU RGB-D 120数据集的两种划分标准Cross-subject和Cross-setup上的识别准确率分别为84.1%和86.0%。与其他算法相对比，本文算法提取到了更为多样化、多尺度的动作特征，动作类别的识别准确率有一定的提升。

Aiming at the problems of insufficient feature extraction, incompleteness and low recognition accuracy in action recognition based on human skeleton sequence, a action recognition model based on multi-branch feature and multi-scale spatio-temporal feature is proposed in this paper. Firstly, the original data are enhanced by the combination of various algorithms. Secondly, the multi-branch feature input form is improved to multi-branch fusion feature information, which is input into the network, respectively. After a certain depth of network modules, it is fused together. Then, a multi-scale spatio-temporal convolution module is constructed as the basic module of the network to extract multi-scale spatio-temporal features. Finally, the overall network model is constructed to output action categories. The experimental results show that the recognition accuracy on Cross-subject and Cross-view of NTU RGB-D 60 data set is 89.6% and 95.1%, and the recognition accuracy on Cross-subject and Cross-setup of NTU RGB-D 120 data set is 84.1% and 86.0%, respectively. Compared with other algorithms,the more diversified and multi-scale action features are extracted, and the recognition accuracy of action categories is improved to a certain extent.