The motor learning mechanism of mammals has been extensively studied, and the learning speed of canines for relevant tasks can be accelerated by conducting guided training on them. According to the above inspiration, this paper proposes a reinforcement learning algorithm based on desired state reward guidance(DSG-SAC) on the basis of soft actor-critic algorithm(SAC). This algorithm uses the state feedback mechanism in the environment to guide the quadruped robot to explore effectively, which can improve the bionic gait learning effect of the quadruped robot and improve the training efficiency. In this algorithm, the strategy network and the evaluation network first approximate the error between the desired state observation and the current state, and after the positive feedback from the current state, the evaluation function and the action are output, so that the quadruped robot moves in the desired direction. In this thesis, the algorithm is verified on a quadruped robot, and the experimental results can be concluded that the proposed algorithm can complete the bionic gait learning of the quadruped robot. Ablation experiments are designed to investigate the effects of hyperparametric temperature coefficients and discount factors on the algorithm, and finally experiments are designed to verify that the improved algorithm has superior performance than the simple SAC algorithm.