Most existing studies on person reidentification (Re-ID) utilize deep feature representation learning. However, images often contain occlusion situations and nondiscriminative personal information. To extract more representative features, some researchers extract implicit deep semantic information by designing complex modules, such as mask maps and human pose landmarks. However, this can introduce complex human annotation and computational work. To overcome these issues, we propose a Re-ID model called multifeature fusion network (MFFNet). Our network does not require any additional auxiliary information and incorporates two new designs: the feature refinement pooling block (FRPB) and the feed-forward conduction structure (FCS). Based on the “split-learn-merge” principle, the FRPB decomposes a person’s features into coarse-grained to fine-grained representations. The FRPB learns the corresponding local detail information and merges the multigranular features into the partial person representation. To address the issue of most current methods heavily relying on accurate bounding boxes, the FCS enables character matching at different resolution scales by learning multiple semantic levels of representation. Through a series of ablation experiments, we demonstrate that the proposed strategy is effective for person Re-ID tasks. The results indicate that MFFNet achieves more competitive experimental results than the existing state-of-the-art methods.