Aiming at the problem that the traditional path planning method can not plan the optimal path according to the timevarying characteristics of urban road network weight, a timevarying road network path planning method based on double deep Qnetwork was proposed. Firstly, the urban road network model with timevarying weights is constructed, in which the weights at each time period of the road segment are generated by random functions. Then, the state features, interaction actions and reward functions are designed to model the timevarying weight network path planning problem, and DDQN algorithm is used to train the agent to learn the timevarying weight characteristics of the road network. Finally, the path is planned according to the modeled state features to realize the effective path planning of the timevarying weight network. The experimental results show that the agent trained by DDQN algorithm has better global optimization ability in the timevarying weight road network. Compared with the rolling path planning algorithm, the proposed method can plan the optimal path under different circumstances, which provides a new idea for the path planning of the road network with timevarying weights.