Abstract: In a large-scale Weapon-Target Assignment (WTA) problem, the explored solution space becomes enormous due to the curse of dimensionality, and it causes low-efficiency in searching optimization solution. For solving this problem effectively, a WTA optimization approach based on multi-attribute decision-making and Deep Q-Network (DQN) is proposed. Firstly, a threat-assessment model for attacking missiles is built based on the approach of Analytic Hierarchy Process (AHP). Meanwhile, an entropy method, used for evaluating the differences of target attributes, is introduced, to increase objective in computing threat-assessment results. Then, an assignment criterion of maximum intercept probability is designed based on assess results, and a multi-steps WTA decision model is built in DQN frame. A uniform experience sampling strategy is designed, making sure that each target type of assignment experience has the same probability to be selected. Furthermore, for balancing the DQN convergence speed and global optimum, a reward function that combines local and global rewards is designed. Lastly, simulation results shows that the proposed WTA approach has the advantage in solving large-scale WTA problem fast and effectively, compared with the general heuristic approach. Also, it presents the robust performance for WTA scenario elements variation.