Opinion　retrieval　is　a　hot　topic　in　the　research　of　natural　language　processing.　Most　existing　approaches　in　text　opinion　retrieval　can　not　extract　knowledge　and　concept　from　context.　They　also　lack　opinion　generalization　ability　and　overlook　the　semantic　relations　between　words.　This　paper　proposes　an　opinion　retrieval　method　based　on　knowledge　graph　conceptualization　and　network　embedding.　First,　conceptual　knowledge　graph　is　used　to　conceptualize　the　queries　and　texts　into　the　correct　conceptual　space　while　the　nodes　in　the　knowledge　graph　are　embedded　into　low　dimensional　vectors　space　by　network　embedding　technology.　Then,　the　similarity　between　queries　and　texts　is　calculated　based　on　embedding　vectors.　According　to　the　similarity　score,　the　opinion　scores　of　texts　can　be　captured　based　on　statistical　machine　learning　methods.　Finally,　the　concept　space,　knowledge　representation　space,　and　opinion　mining　result　serve　opinion　retrieval　models.　The　experiment　shows　that　the　retrieval　model　proposed　in　this　paper　can　effectively　improve　the　retrieval　performance　of　multiple　retrieval　models.　Compared　with　referenced　method　based　on　unified　opinion,　the　proposed　approach　improves　the　MAP　scores　by　6.1%　and　9.3%,　respectively.　Compared　with　referenced　method　based　on　learning　to　rank,　proposed　approach　improves　the　MAP　scores　by　2.3%　and　14.6%,　respectively.　©　Copyright　2018,　Institute　of　Software,　the　Chinese　Academy　of　Sciences.　All　rights　reserved.