This paper studies the problem of the stealthy engagement maneuvering strategy in continuous state space based on deep reinforcement learning.A stealthy engagement maneuvering strategy based on Double Deep Q Network (DDQN) by using the Markov decision process is established.The method of generating a target value function by DDQN solves the over-fitting problem of the traditional DQN.The training sample is obtained by the method of random sampling according to the priority, which accelerates the training of the neural network.The greedy coefficient is set according to the method of exponential decline, which solves the “exploration and utilization dilemma”of traditional reinforcement learning.An angle factor is introduced in the design of the reward function to make it more consistent with the actual combat situation.The simulation results show that DDQN has good convergence and can effectively generate the stealthy engagement maneuvering strategy.