Aiming　at　the　problems　that　current　few-shot　learning　algorithms　are　prone　to　overfitting　and　insufficient　generalization　ability　for　cross-domain　cases,and　inspired　by　the　property　that　reservoir　computing　(RC)　does　not　depend　on　training　to　alleviate　overfitting,a　few-shot　image　classification　method　based　on　reservoir　computing(RCFIC)　is　proposed.　The　whole　method　consists　of　a　feature　extraction　module,a　feature　enhancement　module　and　a　classifier　module.　The　feature　enhancement　module　consists　of　a　RC　module　and　an　attention　mechanism　based　on　the　RC,which　performs　channel-level　enhancement　and　pixel-level　enhancement　of　the　features　of　the　feature　extraction　module,respectively.　Meanwhile,the　joint　cosine　classifier　drives　the　network　to　learn　feature　distributions　with　high　inter-class　variance　and　low　intra-class　variance　properties.　Experimental　results　indicate　that　the　algorithm　achieves　at　least　1.　07%　higher　classification　accuracy　than　the　existing　methods　in　Cifar-　FS,FC100　and　Mini-ImageNet　datasets,　and　outperforms　the　second-best　method　in　cross-domain　scenes　from　Mini-ImageNet　to　CUB-200　by　at　least　1.　77%.　Meanwhile,the　ablation　experiments　verify　the　effectiveness　of　RCFIC.　The　proposed　method　has　great　generalization　ability　and　can　effectively　alleviate　the　overfitting　problem　in　few-shot　image　classification　and　solve　the　cross-　domain　problem　to　a　certain　extent.