Aiming at the problem that it is difficult to reflect the battlefield situation game situation in dynamic Weapon-Target Assignment(WTA)，a Tent Random Particle Swarm Optimization WTA algorithm based on Deep Q Network (DQN) is proposed，namely TRPSO-DQN.Firstly，a DQN is used to generate a game matrix based on the battlefield situation，and then，an improved particle swarm algorithm is used to solve the Nash equilibrium，so as to solve the problems that the traditional linear programming results are inaccurate and the large dimensional matrix cannot be solved.Finally，the proposed TRPSO-DQN algorithm is used to realize Dynamic WTA(DWTA).Experiments show that the algorithm is highly adversarial in realizing DWTA，the results of WTA are more reasonable，the convergence speed of the algorithm is faster，and the Nash equilibrium solution accuracy is better than other algorithms.