When Q-learning is applied to the path planning problem, due to the randomness of action selection and the limited update range of the Q table, the agent will repeatedly explore sub-optimal states and paths, resulting in slower algorithm convergence. To address this problem, this paper introduces an ant colony pheromone aided Q-learning path planning algorithm, an optimization method for the optimization range is proposed to reduce the invalid exploration times of the agent. In addition, in order to improve the purpose of the initial iteration of the algorithm, according to the characteristics of the relationship between the current grid and the end point and the selection of the agent's action, an initialization method of the Q table is designed. In order to make the algorithm have suitable exploration probability in the early, middle and late stages of operation, a method of dynamically adjusting the exploration factor is designed in combination with the concentration of pheromone. Finally, in a variety of environments with different specifications and different characteristics, the effectiveness and feasibility of the proposed algorithm are verified by simulation experiments.