Skeleton-based human action recognition has attracted increasing attention in recent years. However, most existing methods focus on single-person scenarios and struggle with complex behaviors in multi-person groups. In particular, they lack the capability to automatically identify and model core person. To address these challenges, this paper proposes a star-shaped group interaction model for skeleton-based action recognition. Firstly, the character importance scoring system analyzes both individual and group aspects: it evaluates each person's individual importance based on motion intensity and motion complexity, and assesses their significance within the group using centrality and interactivity. This process enables accurate identification of the core person in the video. Secondly, a core-star interaction graph is constructed with the core person as the center node and other individuals as peripheral nodes. The relationships among individuals are categorized into self-connections, centripetal connections, and centrifugal connections. For each type of connection, we design differentiated data augmentation strategies to fully exploit diverse action and interaction features. Finally, the structured skeleton data is fed into the star-shaped spatio-temporal graph convolutional network for efficient feature extraction and action classification. Experiments on several public benchmark datasets demonstrate that our method achieves state-of-the-art performance, achieving accuracies of 79.1%, 96.1%, and 93.1% on the NBA, Volleyball, and Volleyball-weak datasets, respectively.