To address the operational challenges posed by the high penetration of photovoltaic systems in distribution networks—such as system congestion, voltage violations, and increased distribution losses—this study proposes a spatiotemporal sparsified dynamic reconfiguration scheduling method considering practical implementation in real distribution system operations. The proposed framework comprises two complementary sparsification mechanisms. Spatial sparsification is achieved by clustering hourly net-load distributions in a high-dimensional net-load space to aggregate characteristic net-load patterns, thereby restricting power flow evaluations and configuration screening to a small set of representative patterns and substantially reducing the computational burden. Temporal sparsification is realized by solving an integer linear programming problem to optimize the reconfiguration schedule under a daily reconfiguration frequency constraint, which optimizes the reconfiguration timing while mitigating excessive switching operations. Numerical experiments under deterministic forecast assumptions demonstrated that the proposed method can effectively eliminate congestion and voltage violations while achieving loss reduction by 4.56% and 27.4% respectively in two scenarios from the conventional method with the computational scalability significantly improved.