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Aiming at the problems of large trajectory deviation and low efficiency of existing intelligent control algorithms for biped robots, a control algorithm based on D-DQN reinforcement learning is proposed. Firstly, the coordinate transformation relationship in the motion of biped robot and the compensation process of joint and link are analyzed, and then the dimensionality reduction of complex nonlinear motion process is realized based on Q-value network. The double weight design method of Q-value network weight and auxiliary weight is adopted to strengthen the performance of DQN network, and Tanh function is used as the activation function of neural network to improve the numerical training ability of DQN network. The experience playback pool plays a key auxiliary role in data training and interaction. By inputting the reward value into the objective function, the control accuracy of the biped robot is further improved. Finally, the stability of the biped robot is improved by virtual constraint control. The experimental results show that under the D-DQN reinforcement learning control algorithm, the time of the robot to complete the first stage test is only 115s, the comprehensive trajectory deviation is 0.02m, and the stability of the gait switching limit cycle test is good.