In response to the challenges posed by existing action recognition methods that rely on skeleton information,particularly their low utilization of skeletal sequence data and difficulties in accurate recognition, this paper presents a novel action recognition approach utilizing a graph convolutional bidirectional long short-term memory (GCN-Bi-LSTM) network enhanced by an attention mechanism.Firstly,a nonphysical dependency relationship is constructed by leveraging the relative distances between skeleton nodes to enrich skeletal features.Secondly,a spatio-temporal graph convolutional network is employed to extract features from each video frame,thereby obtaining more sophisticated semantic representations.Finally, these frame-specific features are fed into the bidirectional LSTM network augmented with an attention mechanism to capture global temporal characteristics and then action discrimination capabilities can be enhanced effectively.Experimental results show that the proposed method can significantly improve recognition accuracy and has great potential for application in tactical action analysis and training scenarios.