In　order　to　solve　the　problems　of　difficulties　and　low　detection　accuracy　caused　by　the　small　sample　size　and　variable　target　scale　of　wildlife　datasets,a　few-shot　wildlife　detection(MS-FSWD）　algorithm　based　on　multi-scale　context　extraction　was　proposed.　Firstly,the　multi-scale　context　extraction　module　was　used　to　enhance　the　perception　ability　of　the　model　for　wildlife　at　different　scales　and　improve　the　detection　performance.　Secondly,Res2Net　was　introduced　as　a　strong　classification　network　of　the　prototype　calibration　module　to　correct　the　class　scores　output　by　the　classifier.　Then,the　shuffle　attention　mechanism　was　added　to　the　RPN　to　enhance　the　feature　map　of　the　target　region　and　weaken　the　background　information.　Finally,using　the　Balanced　L1　Loss　as　the　localization　loss　function　improves　the　target　positioning　performance.　Experimental　results　show　that　compared　with　the　DeFRCN　algorithm,　MS-FSWD　improves　the　novel　class　AP50　by　9.　9%　and　6.　6%　respectively　in　the　1-shot　and　3-shot　detection　tasks　on　the　few-shot　wildlife　dataset　FSWA.　On　the　public　dataset　PASCAL　VOC,MS-FSWD　is　increased　by　up　to　12.　6%.　Compared　with　the　VFA　algorithm,in　the　10-shot　task　of　the　PASCAL　VOC　dataset　Novel　Set　3,the　novel　class　AP50　is　increased　by　3.　3%.　©　2025,　Science　Press.　All　rights　reserved.