This　study　proposes　an　identification　model　based　on　subject-action-object　(SAO)　structure　semantic　analysis　for　the　potential　hotspots　of　technology　demand　to　address　the　shortcomings　of　technology　demand　mining　on　the　basis　of　word　frequency　statistical　analysis.　The　SAO　structure　is　extracted　using　Python　tools　to　identify　the　potential　hotspots　of　technology　demand,　the　domain　dictionary　and　professional　corpus　are　introduced,　and　the　clustering　of　technology　demand　is　realized　by　applying　Word2Vec　and　HowNet　to　calculate　the　semantic　similarity　among　the　SAO　structures.　The　layout　of　the　technology　demand　in　the　different　stages　of　the　technical　lifecycle　is　divided　by　constructing　a　technology　map.　The　proposed　model　is　validated　as　an　example　of　the　network　technology　demand　text　of　the　new　energy　and　energy　saving　fields.　Therefore,　the　hotspots　of　technology　demand　are　the　technology　of　new　energy　vehicle　motor　and　its　control　system,　technology　of　energy　efficient　and　technology　of　wind　power,　and　the　new　energy　vehicle　technology　is　still　in　the　research　and　development　(R&D)　stage.　Moreover,　solar　energy　products　and　production　equipment　are　still　in　the　technical　application　stage.　This　study　provides　an　effective　method　for　identifying　potential　technology　demand　and　based　on　technology　lifecycle　to　implement　the　layout　and　visualization　of　demand,　which　will　make　the　decision　support　for　guiding　the　direction　of　technology　R&D,　optimizing　the　allocation　of　science　and　technology　resources,　and　promoting　the　effective　docking　of　technology　supply　and　demand.