Topography　can　strongly　affect　ground　motion,　and　studies　of　the　quantification　of　hill　surfaces＇　topographic　effect　are　relatively　rare.　In　this　paper,　a　new　quantitative　seismic　topographic　effect　prediction　method　based　upon　the　BP　neural　network　algorithm　and　three-dimensional　finite　element　method　(FEM)　was　developed.　The　FEM　simulation　results　were　compared　with　seismic　records　and　the　results　show　that　the　PGA　and　response　spectra　have　a　tendency　to　increase　with　increasing　elevation,　but　the　correlation　between　PGA　amplification　factors　and　slope　is　not　obvious　for　low　hills.　New　BP　neural　network　models　were　established　for　the　prediction　of　amplification　factors　of　PGA　and　response　spectra.　Two　kinds　of　input　variables＇　combinations　which　are　convenient　to　achieve　are　proposed　in　this　paper　for　the　prediction　of　amplification　factors　of　PGA　and　response　spectra,　respectively.　The　absolute　values　of　prediction　errors　can　be　mostly　within　0.1　for　PGA　amplification　factors,　and　they　can　be　mostly　within　0.2　for　response　spectra＇s　amplification　factors.　One　input　variables＇　combination　can　achieve　better　prediction　performance　while　the　other　one　has　better　expandability　of　the　predictive　region.　Particularly,　the　BP　models　only　employ　one　hidden　layer　with　about　a　hundred　nodes,　which　makes　it　efficient　for　training.