In　recent　years,　hash　is　a　popular　method　of　image　retrieval.　Convolutional　neural　network　is　used　to　generate　image　features　and　hash　codes,　so　as　to　achieve　fast　and　effective　image　retrieval.　In　order　to　solve　the　problem　of　poor　image　retrieval　effect　caused　by　complex　background　of　Multi-target　Image,　this　paper　proposes　a　hash　generation　method　based　on　the　combination　of　target　region　recommendation　network　and　convolutional　neural　network.　The　RPN　network　is　selected　as　the　target　region　recommendation　algorithm.　After　the　image　passes　through　the　RPN　network,　multiple　target　regions　and　the　four-dimensional　coordinates　of　each　region　will　be　generated.　According　to　the　four-dimensional　coordinates,　the　main　target　region　will　be　filtered　and　extracted.　Then,　the　feature　of　the　largest　target　area　is　extracted　by　the　convolutional　neural　network　(GoogleNet),　and　the　corresponding　hash　code　is　generated,　which　is　finally　retrieved　in　the　database.　Experiments　are　carried　out　on　voc2012　data　set　and　self　collected　data　set　to　verify　the　algorithm.　When　the　number　of　test　images　is　1000,　the　experimental　results　show　that　the　total　correct　rate　of　the　retrieval　results　of　this　method　is　95.5%,　which　is　about　5　percentage　points　higher　than　the　existing　methods.　©　Published　under　licence　by　IOP　Publishing　Ltd.