Efficient mobile charging scheduling is a key technology to build wireless rechargeable sensor networks (WRSN) which have long life cycle and sustainable operation ability. The existing charging methods based on reinforcement learning only consider the spatial dimension of mobile charging scheduling, i.e., the path planning of mobile chargers (MCs), while leaving out the temporal dimension of the problem, i.e., the adjustment of the charging duration, and thus these methods have suffered some performance limitations. This study proposes a dynamic spatiotemporal charging scheduling scheme based on deep reinforcement learning (SCSD) and establishes a deep reinforcement learning model for dynamic adjustment of charging sequence scheduling and charging duration. In view of the discrete charging sequence planning and continuous charging duration adjustment in mobile charging scheduling, the study uses DQN to optimize the charging sequence for nodes to be charged and calculates and dynamically adjusts the charging duration of the nodes. By optimizing the two dimensions of space and time respectively, the SCSD proposed in this study can effectively improve the charging performance while avoiding the power failure of nodes. Simulation experiments show that SCSD has significant performance advantages over several well-known typical charging schemes.