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　feature　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　granularities　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　representation.　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　supervised　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　capture　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).　©　2023　Elsevier　Ltd