In order to reduce the impact force of swing legs and improve walking stability when a small humanoid robot is walking, a set of impact dynamics equations based on the second kind Lagrange equation is produced, and an impact compensation control strategy with a BP network optimized by a particle swarm algorithm is designed. The core element of the compensation controller is replacing the error back propagation with a particle swarm algorithm. Due to the regulating joints of the knee, hip and ankle, the walking process is more stable than before. The experiment results show that when the left swing leg lands, the impact force drops by 2N and 1.5N respectively in the moments 4.5s and 10.5s. Therefore, the compensation strategy can reduce the impact force effectively and improve the walking stability.