Fine-grained　visual　classification　(FGVC)　aims　to　identify　objects　belonging　to　multiple　sub-categories　of　the　same　super-category　(such　as　species　of　birds,　models　of　cars　and　aircraft).　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　Multi-Granularity　Feature　Distillation　Learning　Network　(MGFDL-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　strengthen　the　connection　between　high-level　semantics　and　low-level　semantics　for　robust　multi-granularity　feature　learning.　Comprehensive　experiments　show　that　our　method　achieves　state-of-the-art　performance　on　three　challenging　fine-grained　visual　classification　FGVC　datasets　(CUB-200-2011,　Stanford　Cars　and　FGVC-　Aircraft).