Few　shot　learning　aims　to　recognize　novel　categories　with　only　few　labeled　data　in　each　class.　We　can　utilize　it　to　solve　the　problem　of　insufficient　samples　during　training.　Recently,　many　methods　based　on　meta-learning　have　been　proposed　in　few　shot　learning　and　have　achieved　excellent　results.　However,　unlike　the　human　visual　attention　mechanism,　these　methods　are　weak　in　filtering　critical　regions　automatically.　The　main　reason　is　meta-learning　usually　treats　images　as　black　boxes.　Therefore,　inspired　by　the　human　visual　attention　mechanism,　we　introduce　the　salient　region　into　the　few　shot　learning　and　propose　the　SRFS-Net.　In　addition,　considering　the　introduction　of　the　salient　region,　we　also　modify　the　embedding　function　to　improve　the　feature　extraction　capabilities　of　the　network.　Finally,　the　experimental　results　in　miniImagenet　dataset　show　that　our　model　performs　better　in　5-way　1-shot　than　few　shot　learning　models　in　recent　years.　©　2021　ACM.