To address the issue of missing dynamic interactions between units caused by existing intent recognition methods failing to account for formation spatial characteristics, this paper introduces a spatiotemporal coupling mechanism and proposes a formation intent recognition method that integrates dynamic graph attention mechanisms with spatiotemporal modeling. First, a global interaction backbone network is established based on target attributes within the formation, combined with a Top-K nearest neighbor strategy to dynamically generate an adjacency matrix. This transforms the dynamically evolving formation state into a structured temporal graph. Second, a Graph Attention Network (GAT) enhanced with a Co-evolution Aware Pooling mechanism is employed to adaptively learn differentiated interaction weights between different targets, enabling rapid capture of spatial coordination features in the dynamic formation temporal graph. Finally, a Bidirectional Gated Recurrent Unit (BiGRU) augmented with a MultiScale Attention mechanism is introduced to perform temporal analysis on the node state sequences extracted by GAT, which contain spatial coordination information. This establishes an intent recognition model that deeply integrates spatiotemporal features (STGAT-BiGRU). Simulation results show that, compared to existing methods, the proposed approach achieves average improvements of 8.78% and 8.9% in accuracy and F1 score, respectively, demonstrating its effectiveness and providing technical support for mastering situation evolution and gaining decision-making initiative.