A　neural　network　prediction　model　for　NMOFET　device　is　proposed　in　this　paper　by　using　BP　algorithm　in　machine　learning　technology　and　Silvaco　TCAD　simulation　tool,　so　as　to　improve　the　efficiency　of　actual　simulation　work　and　provide　a　new　method　for　the　performance　research　of　NMOSFET.　In　the　process　of　modeling,　the　substrate　bias,　the　impurity　concentration　of　substrate,　the　thickness　of　oxide　and　the　threshold　voltage　adjustment　implant　doping　concentration　are　regarded　as　independent　variable,　and　substituted　into　the　simulation　software　for　simulation.　The　performance　parameters　of　NMOSFET　are　obtained　as　the　dependent　variable,　such　as　the　threshold　voltage,　the　maximum　transconductance,　the　subthreshold　slope　and　the　Ion/Ioff　ratio.　All　the　simulation　results　are　used　to　assisted　the　training　and　prediction　of　BP　neural　network.　The　results　of　experiment　show　that　the　BP　neural　network　prediction　model　with　Levenberg-Marquardt　algorithm　can　well　predict　the　performance　of　NMOSFET　devices.　The　determination　coefficient　of　all　models　is　above　0.99,　and　the　mean　absolute　percentage　error　of　all　models　is　below　1.5%.