With the development of technologies such as artificial intelligence, multi-agents ( e. g. , unmanned aerial vehicle swarms) have been increasingly applied in practical combat operations. The Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, designed to solve the coordination problems of multi-agents in cooperative environments, has become one of the mainstream applied algorithms in the multi-agent field owing to its unique Actor-Critic framework. To address the problems in multi-agent collaborative tasks during command and decision-making—including ambiguous role division and slow convergence of the algorithm's policy caused by information overload—an improved MADDPG algorithm incorporating a Dynamic Role Attention(DRA) mechanism, namely DRA-MADDPG, is proposed. This algorithm embeds a DRA module into the Actor-Critic framework, and achieves accurate optimization of division of labor and collaboration by dynamically adjusting the attention weights of each agent towards peers with different roles. Specifically, the role set ( reconnaissance, assault, command) and phase division ( exploration→execution→encirclement) for command tasks are defined, and on this basis, a role coordination matrix and phase adjustment coefficients are constructed. A DRA module is designed in the Critic network to calculate weights and filter key information by leveraging role relevance and task phases. Additionally, the Actor network is improved to generate targeted actions by integrating role responsibilities. Simulation experiments show that compared with MADDPG, the Area Under the Curve (AUC) of the cumulative training reward of DRA-MADDPG increases by 2.4%, and the task completion time decreases by 19.3%. Furthermore, comparative analysis of training reward curves reveals that DRA-MADDPG exhibits better learning efficiency in short-term training. It is demonstrated that this method is suitable for complex command and decision-making scenarios and provides a relatively efficient solution for multi-agent coordination.