多智能体强化学习是当前实现多机自主协同空战最具潜力的方法之一。然而现有方法受限于端到端网络结构，在空战中存在多机协同性差和难以反映决策动机的关键性问题。为此，本文提出一种时空信息融合的多机协同空战决策方法以提升多机空战的协同性与可解释性。首先，设计了一种基于图注意力机制的空间信息融合方法聚合智能体局部观测并形成全局态势评估，解决全连接评价网络动态适应性弱的问题。其次，设计了一种交叉注意力和门控循环单元的时空信息融合方法聚合敌友方单元信息和时序信息，为策略网络融合协同性特征。最后，结合强化学习构建了时空信息融合的多机协同空战决策算法，并在高保真空战环境下进行了验证。实验结果表明，所提方法具有较强的协同性和决策动机的可解释性。

Multi-agent reinforcement learning is currently one of the most promising methods for realizing multi-UAV autonomous cooperative air combat. However, existing methods are constrained by the end-to-end network architecture, facing critical issues such as poor multi-UAV coordination and difficulty in reflecting decision-making motivation in air combat. To address this, this paper proposes a multi-UAV cooperative air combat decision-making method based on spatial-temporal information fusion to improve the cooperation and interpretability of multi-aircraft air combat. First, a spatial information fusion method based on graph attention mechanism is designed to aggregate local observations of agents and form global situation assessment, solving the problem of weak dynamic adaptability of fully connected critic networks. Second, a spatial-temporal information fusion method combining cross-attention and gated recurrent unit is developed to aggregate information of enemy and friendly units and temporal information, fusing coordination features for the policy network. Finally, a spatial-temporal information fusion-based multi-UAV cooperative air combat decision-making algorithm is constructed by integrating reinforcement learning and validated in a high-fidelity air combat environment. Exper-imental results show that the proposed method exhibits strong coordination and interpretability of decision-making motivation.