Few-shot　segmentation　focuses　on　the　generalization　of　models　to　segment　unseen　object　with　limited　annotated　samples.　However,　existing　approaches　still　face　two　main　challenges.　First,　a　huge　feature　distinction　between　support　and　query　images　a　causes　knowledge　transferring　barrier,　which　harms　the　segmentation　performance.　Second,　limited　support　prototypes　cannot　adequately　represent　features　of　support　objects,　hard　to　guide　high-quality　query　segmentation.　To　deal　with　the　above　two　issues,　we　propose　a　self-distillation　embedded　supervised　affinity　attention　model　to　improve　the　performance　of　few-shot　segmentation　task.　Specifically,　the　self-distillation　guided　prototype　module　uses　self-distillation　to　align　the　features　of　support　and　query.　The　supervised　affinity　attention　module　generates　a　high-quality　query　attention　map　to　provide　sufficient　object　information.　Extensive　experiments　prove　that　our　model　significantly　improves　the　performance　compared　to　existing　methods.　Comprehensive　ablation　experiments　and　visualization　studies　also　show　the　significant　effect　of　our　method　on　the　few-shot　segmentation　task.　On　COCO-　20(i)　data　set,　we　achieve　new　state-of-the-art　results.　Training　code　and　pretrained　models　are　available　at　https://github.com/cv516Buaa/SD-AANet.