It　is　an　inevitable　trend　for　the　development　of　global　digital　economy　to　transform　data　into　data　assets　and　realize　their　transaction　circulation.　Aiming　at　the　release　of　data　value　and　the　development　of　its　transaction　process,　the　concept　of　integrated　score　of　data　is　proposed　by　combining　integrated　quality　index　containing　four　dimensions　with　data　quantity.　On　this　basis,　data　assets　are　priced　according　to　the　principle　of　profit　maximization　by　constructing　a　nonlinear　programming　model.　Among　them,　three　types　of　pricing　models　are　divided　according　to　the　heterogeneity　of　consumers＇　utility　sensitivity,　and　the　consumers＇　wiilingness　to　pay　are　adjusted　based　on　business　parameters　using　FAHP　system.　The　proposed　model　is　verified　with　the　data　of　China＇s　carbon　emissions　as　the　original　data,　combined　with　the　KNN　machine　learning　algorithm　and　a　series　of　simulation　analyses.　In　addition,　multiple　sets　of　heterogeneous　data　are　tested.　The　results　show　that　the　quality,　quantity　and　utility　of　data　have　an　important　impact　on　the　pricing　of　data　assets,　and　it　is　necessary　to　divide　the　utility　sensitivity　of　consumers　as　well　as　take　business　parameters　into　consideration.　The　model　proposed　can　also　provide　decision-making　reference　for　data　platforms.