In this paper, a new adversarial imitation learning algorithm is proposed based on Weighted Wasserstein distance (WWDAIL). This algorithm employs an enhanced proximal optimization strategy to improve the efficiency and stability of parameter updates. Furthermore, the weighted Wasserstein distance is utilized as a metric for assessing policy differences, thereby increasing the flexibility and dynamism of the measurement. Furthermore, by refining the reward function derived from the discriminator’s output, the reward collection capability and efficiency are significantly enhanced. Experimental results in the Mujoco simulation environment have demonstrated that the proposed algorithm exhibits substantial stability in continuous control tasks and markedly improves the agent’s reward collection ability and efficiency.