The spatial and temporal conflicts within terminal maneuvering areas, particularly in multi-airport systems, are growing increasingly complex. Traditional independent processing methods face inherent limitations when dealing with multi-source uncertainties, dynamic weather conditions, and high-density operations. This paper proposes MSTAGNN-MARL that systematically integrates the resolution of spatial conflicts and temporal scheduling issues. This framework is based on four crucial innovations: First, a strategic-tactical-execution hierarchical architecture is constructed that integrates multi-criteria decision optimization with graph neural network-based multi-agent reinforcement learning. Second, an uncertainty perception mechanism is designed that explicitly encodes conflict features as dynamic edge attributes in social graphs, incorporating a real-time dynamic weather model and a Gaussian noise-based perception uncertainty model. Third, develop a compliance automated system for behavior cloning that learns the decision preferences of controllers to achieve human–machine collaboration and provide transparent visualization. Fourth, a robustness assurance mechanism for abnormal scenarios is constructed, employing behavior tree-driven emergency strategies to handle unexpected situations. Experiments demonstrate that the proposed method achieves an 89.3% conflict resolution rate, reduces average delays by 6 min compared to existing methods, and exhibits robust performance under varying traffic densities and dynamic weather conditions. Ablation experiments validate the effectiveness of the four innovations. This framework provides a new research paradigm for scheduling and decision-making in Intelligent Transportation Systems (ITS).