Urban public transportation, epitomized by buses, has become integral in facilitating efficient and convenient mobility for citizens. Despite its growth, the prevalent bus operation paradigm (static departure schedules, linear route planning, underutilized spatio-temporal data, etc.) often fails to cater to the fluid travel needs of passengers. This study presents a novel dynamic scheduling method based on spatial graph convolution and proximal policy optimization, called DSPTR. Combining a bus dynamic scheduling model (BDS) and a passenger path planning model (PRP), DSPTR method judiciously determines bus departure intervals and formulates passenger travel routes. Leveraging the Weight Graph Sample and AggreGatE (WGraphSAGE), DSPTR adeptly extract road network characteristic, ensuring the feature representation quality of bus resource states. The Proximal Policy Optimization (PPO) further refines the scheduling by optimizing bus multi-route departures and passenger route recommendations. Through experiments and evaluations on real data in Shanghai, DSPTR outperforms baselines such as DDPG and A2C. Through the traffic resource scheduling plan generated by DSPTR, bus operating costs and passenger travel times can be significantly reduced, highlighting its potential to improve the operational efficiency of the urban public transportation system.