Fine-grained　visual　classification　(FGVC)　aims　to　identify　objects　belonging　to　multiple　sub-categories　of　the　same　super-category.　The　key　to　solving　fine-grained　classification　problems　is　to　learn　discriminative　visual　feature　representation　with　only　subtle　differences.　Although　previous　work　based　on　refined　fea-ture　learning　has　made　great　progress,　however,　high-level　semantic　features　often　lack　key　information　for　fine-grained　visual　object　nuances.　How　to　efficiently　integrate　semantic　information　of　different　gran-ularities　from　classification　networks　is　a　critical.　In　this　paper,　we　propose　Granularity-aware　Distillation　and　Structure　Modeling　region　Proposal　Network(GDSMP-Net).　Our　solution　integrates　multi-granularity　hierarchical　information　through　a　multi-granularity　fusion　learning　strategy　to　enhance　feature　repre-sentation.　In　view　of　the　inherent　challenges　of　large　intra-class　differences　in　FGVC,　a　cross-layer　self-distillation　regularization　is　proposed　to　to　strengthen　the　connection　between　high-level　semantics　and　low-level　semantics　for　robust　multi-granularity　feature　learning.　On　this　basis,　we　use　a　weakly　super-vised　method　to　generate　local　branches,　and　the　collaborative　learning　of　discriminative　semantics　and　structural　semantics　based　on　local　regions,　facilitating　model　to　perceive　contextual　information　to　cap-ture　structural　interactions　between　local　semantics.　Comprehensive　experiments　show　that　our　method　achieves　state-of-the-art　performance　on　four　widely-used　challenging　datasets.(CUB-200-2011,　Stanford　Cars,　FGVC-Aircraft　and　NA-birds).　(c)　2023　Elsevier　Ltd.　All　rights　reserved.