Few-shot　object　detection　achieves　rapid　detection　of　novel-class　objects　by　training　detectors　with　a　minimal　number　of　novel-class　annotated　instances.　Transfer　learning-based　few-shot　object　detection　methods　have　shown　better　performance　compared　to　other　methods　such　as　meta-learning.　However,　when　training　with　base-class　data,　the　model　may　gradually　bias　towards　learning　the　characteristics　of　each　category　in　the　base-class　data,　which　could　result　in　a　decrease　in　learning　ability　during　fine-tuning　on　novel　classes,　and　further　overfitting　due　to　data　scarcity.　In　this　paper,　we　first　find　that　the　generalization　performance　of　the　base-class　model　has　a　significant　impact　on　novel　class　detection　performance　and　proposes　a　generalization　feature　extraction　network　framework　to　address　this　issue.　This　framework　perturbs　the　base　model　during　training　to　encourage　it　to　learn　generalization　features　and　solves　the　impact　of　changes　in　object　shape　and　size　on　overall　detection　performance,　improving　the　generalization　performance　of　the　base　model.　Additionally,　we　propose　a　feature-level　data　augmentation　method　based　on　self-distillation　to　further　enhance　the　overall　generalization　ability　of　the　model.　Our　method　achieves　state-of-the-art　results　on　both　the　COCO　and　PASCAL　VOC　datasets,　with　a　6.94%　improvement　on　the　PASCAL　VOC　10-shot　dataset.