Document-level relation extraction (RE) aims to determine the relations between entities scattered across different sentences through reading and reasoning. Existing methods use semantic segmentation to obtain global information among triples by analyzing entity-level matrices. However, complete document input may introduce certain interference, making it challenging to express the underlying relationships. To address this, we propose a novel approach introducing a low-entity redundancy feature map, achieved by removing certain entities. The proposed optimal path filtering (OPF) selects entity-related sentences using heuristic rules and formulates sentence selection as a set cover problem, solved via backtracking pruning. U-Net is then applied to obtain global features. Our experiment achieves state-of-the-art results on two common document-level RE datasets, Re-DocRED and CDR, outperforming previous methods.