Aiming at the problem that biped robots may easily lose their motion stability when walking on uneven ground, a value-based deep reinforcement learning algorithm called Deep Q-Network (DQN) gait control method was proposed, which is an intelligent learning method of posture adjustment. Firstly, an off-line gait for a flat ground environment was obtained through the gait planning of the robot. Secondly, instead of implementing a complex dynamic model compared to traditional control methods, a bipedal robot was regarded as an agent to establish robot environment space, state space, action space and Reward-Punishment (RP) mechanism. Finally, through multiple rounds of training, the biped robot learned to adjust its posture on the uneven ground and ensures the stability of walking. The performance and effectiveness of the proposed algorithm was validated in a V-Rep simulation environment. The results demonstrate that the biped robot's lateral tile angle is less than 3° after implementing the proposed method and the walking stability is improved obviously, which achieves the robot's posture adjustment behavior learning and proves the effectiveness of the method.