Aiming at the bottleneck problems of traditional Wireless Sensor Network(WSN) nodes like limited energy supply and short network life, based on the latest achievements in the field of wireless energy transmission technology, a charging path planning algorithm based on improved Q - Learning Wireless Rechargeable Sensor Network(WRSN) is proposed. Firstly, the base station performs charging task scheduling based on the energy consumption information of each node in the network; and then mathematical modeling and target constraint setting are performed on the path planning problem. The mobile charging vehicle is abstracted as an agent, and its state set and action set are determined. The ε-greedy strategy is reasonably improved for action selection, and the relevant performance parameters are selected to design the reward function. Finally, the state space environment is explored through iterative learning to adaptively obtain the optimal charging path. The simulation results prove that the charging path planning algorithm can quickly converge, and has certain advantages in terms of network life, average charging times of nodes and energy utilization compared with the classic algorithms of the same type.