The　task　of　few-shot　classification　(FSC)　is　to　build　a　model　to　discriminate　novel　categories　that　do　not　present　in　training　categories　with　limited　labeled　samples.　However,　in　many　applications,　the　training　categories　and　novel　categories　are　assumed　to　come　from　the　same　domain.　Existing　few-shot　classification　algorithms　achieve　promising　performance　in　a　single　domain,　but　often　fail　to　generalize　to　unseen　domains　because　of　the　different　feature　distribution　across　domains.　The　main　goal　of　this　work　is　to　propose　an　effective　recognition　model　that　can　work　on　various　image　domains　with　domain　shift.　Specifically,　we　propose　to　construct　a　new　domain-attention　mechanism　with　some　adapters　based　on　the　squeeze-and-excitation　network　architectures.　The　proposed　network　processes　multiple　domains　simultaneously　and　all　parameters　are　shared　across　domains.　In　addition,　because　of　the　large　discrepancy　in　feature　distributions　among　different　domains,　similarity　transformation　layers　are　used　to　reduce　the　differences　in　the　feature　distributions　in　the　training　stage.　Extensive　experiments　were　conducted　to　validate　the　domain　generalization　capability　(GC)　of　this　model　on　four　FSC　datasets:　mini-ImageNet,　Cars,　CUB-200-2011,　and　Simpsons　Characters　Data.　The　results　show　that　the　presented　method　has　excellent　performances　on　various　datasets　across　diverse　domains.&　COPY;　2023　The　Author(s).　Published　by　Elsevier　B.V.　This　is　an　open　access　article　under　the　CC　BY　license　(http://creativecommons.org/licenses/by/4.0/).