Taking　natural　disasters　as　an　example,　this　paper　collects　the　data　of　natural　disasters　in　China　from　2014　to　2020,　and　makes　a　statistical　analysis　on　the　temporal　and　spatial　distribution　of　natural　disasters　in　China.　Four　prediction　models　including　Bayesian　regression,　KNN,　support　vector　machine　and　neural　network　are　established.　The　natural　disaster　risk　prediction　indexes　are　constructed　by　using　the　direct　economic　loss　of　natural　disasters　and　the　affected　population,　and　the　regression　fitting　and　prediction　experiments　are　carried　out.　The　fitting　and　prediction　performance　of　each　model　are　compared　by　using　four　quantitative　evaluation　indexes:　mean　absolute　error,　r2score,　mean　square　error　and　adjusted　R-square.　The　results　show　that　natural　disasters　in　China　show　obvious　temporal　and　spatial　distribution　law,　and　the　prediction　performance　of　neural　network　model　is　the　best.　©　2021　IEEE.