Region　is　a　complicated　system,　where　human,　nature　and　society　interact　and　influence.　Quantitative　modeling　and　simulation　of　ecology　in　the　region　are　the　key　to　realize　the　strategy　of　regional　sustainable　development.　Traditional　machine　learning　methods　have　made　some　achievements　in　the　modeling　of　regional　ecosystems,　but　it　is　difficult　to　determine　the　learning　characteristics　and　to　realize　spatio-temporal　simulation.　Deep　learning　does　not　need　prior　identification　of　training　characteristics,　have　excellent　feature　learning　ability,　can　improve　the　accuracy　of　model　prediction,　so　the　use　of　deep　learning　model　has　a　significant　advantage.　Therefore,　we　use　net　primary　productivity　(NPP),　atmospheric　optical　depth　(AOD),　moderate-resolution　imaging　spectrometer　(MODIS),　Normalized　Difference　Vegetation　Index　(NDVI),　landcover　and　population　data,　and　use　LSTM　to　do　spatio-temporal　simulation.　We　conduct　spatial　analysis　and　driving　force　analysis.　The　conclusions　are　as　follows:　the　ecological　deficit　of　northwestern　Henan　and　urban　communities　such　as　Zhengzhou　is　higher.　The　reason　of　former　lies　in　the　weak　land　productivity　of　the　Loess　Plateau,　the　irrational　crop　cultivation　mode.　The　latter　lies　in　the　high　consumption　of　resources　in　the　large　urban　agglomeration;　The　positive　trend　of　Henan　ecological　development　from　2013　is　mainly　due　to　the　effective　environmental　protection　policy　in　the　12th　five-year　plan;　The　main　driver　of　the　sustained　ecological　deficit　growth　of　Henan　in　2004-2013　is　high-speed　urbanization,　increasing　population　and　goods　consumption.　This　article　provides　relevant　basic　scientific　support　and　reference　for　the　regional　ecological　scientific　management　and　construction.　©　Authors　2017.