Existing human action recognition methods seldom consider the problem of multiplayer interaction in realistic scenarios, which makes them not robust enough for action recognition in the case of multiplayer interaction. To mitigate this issue, we propose a robust skeletal action recognition method based on graph convolution, specifically designed to handle multiplayer interactive actions. Existing methods extract action features of each person independently, and then pool and fuse the independent multiplayer features, which cannot effectively obtain the interaction features among multiplayer and also leads to information loss. In our approach, we construct multiplayer interaction adaptive graphs to effectively establish connections between different entities in multiplayer interactive actions. This enables the network to gather information about the interactions occurring among multiple entities. The experimental results show that our method achieves better performance on the NTU-RGBD dataset, and the recognition accuracy is improved by 4.1% and 5.5% on CS and CV benchmarks, respectively, compared with the baseline method.