This　paper　proposes　a　technology　demand　recognition　model　based　on　semantic　similarity　and　patent　transaction　in　the　view　of　technology　supply　and　demand　text　matching.　Firstly,　we　collect　the　network　technology　demand　text　and　extract　the　key　phrases.　On　this　basis,　we　build　the　patent　transaction　index　library,　retrieve　the　high-related　patents　based　on　the　key　phrases,　build　background　library　of　patent　technology　supply　and　cut　the　patent　title　and　abstract　those　in　the　background　library.　Thirdly,　we　propose　the　method　of　semantic　matching　weight　calculation　of　technical　supply　and　demand　text　based　on　word　vector,　filter　the　effective　technology　demand　and　classification.　Finally,　we　classify　the　clustering　results　in　two　dimensions　based　on　the　amount　of　demand　and　the　corresponding　amount　of　patent　transaction.　By　selecting　the　new　energy　technology　field　as　an　example　to　test　the　model,　it　is　found　that　there　are　195　effective　technology　demands,　they　are　aggregated　into　12　categories　based　on　semantic　similar.　Combined　with　the　amount　of　demand　and　the　corresponding　amount　of　patent　transaction,　the　12　categories　of　technology　demand　are　divided　into　four　categories:　＇high　demand,　high　transaction＇,　＇high　demand,　low　transaction＇,　＇low　demand,　low　transaction＇,　＇low　demand,　low　transaction＇.　The　research　provides　a　new　idea　for　mining　network　technology　demand　and　matching　the　demand　and　supply.　©　2019,　Editorial　Board　of　Journal　of　Systems　Engineering　Society　of　China.　All　right　reserved.