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Intelligently formulating the RPA execution path is conducive to saving labor costs and improving the promotion of RPA for enterprises. For the first time, this paper proposed based on improving DDQN algorithm for RPA path planning. First of all, the problem that the working environment of RPA was a Web page, which didn't meet the exploration conditions of the depth enhancement algorithm, with the help of the idea of metaphor map, it built the virtual environment to meet the requirements of the path planning experiment. At the same time, in order to improve the exploration efficiency of DDQN algorithm, this paper proposed to use the Jaccard coefficient of the location information between samples as a sample priority and combined it with rank-based prioritization to build new sampling methods. This paper randomly used task samples on the virtual environment to verify to demonstrate compliance with the experimental requirements. Further comparison of experimental results of the improved DDQN with DQN, DDQN, PPO and SAC-Discrete shows that the improved algorithm has fewer iterations, faster convergence speed, and higher return value, indicating the effectiveness and feasibility of the improving DDQN algorithm. [ABSTRACT FROM AUTHOR]