Aiming at the optimization of 5G network coverage performance in the complex environment of coal mine underground，a 5G optimization scheme based on deep reinforcement learning is proposed.For the 10km main transport roadway scenario，multiple transmission loss factors such as roadway cross-section size，wall roughness，and equipment occlusion are comprehensively considered，and a signal propagation mathematical model integrating the line-of-sight/nonline-of-sight path loss model and the roughness attenuation factor is established.A deep Q-network is adopted as the value-function approximator for the learning agent，transforming the joint online optimization of base-station placement and transmit power into a multi-objective decision problem that maximizes the coverage while minimizing the number of base stations.Adopt a dynamic power-adjustment mechanism，enabling real-time adaptation to abrupt local signal degradations.Experimental results confirm that the scheme achieves a coverage exceeding 95%，while reducing the number of deployed base stations by 28%compared with a conventional static layout，thereby markedly enhancing the underground 5G coverage and lowering the deployment costs and operational power consumption.