With the widespread adoption of mobile robots, path planning and intelligent navigation have become hot research topics. In order to enhance the globality of traditional robot path planning algorithms, a deep Q-learning network (DQN) algorithm is proposed. By utilizing the reinforcement learning method, mobile robots can effectively navigate to the target location while avoiding the problems in conventional path planning algorithms, such as redundant pathways, decreased continuity and reliance on local information. The proposed method results in a significant reduction in inflection points of the inspection path, mitigating the occurrence of local optima and providing a highly optimized global solution for path planning under known map conditions. In conclusion, the proposed algorithm has been simulated and compared with conventional path planning techniques utilizing a two-dimensional raster map. Empirical findings attest to the dependability of the proposed global optimization technique, which has culminated in the generation of an optimized planned path.