Slope　collapse　is　one　of　the　most　severe　natural　disaster　threats,　and　accurately　predicting　slope　deformation　is　important　to　avoid　the　occurrence　of　disaster.　However,　the　single　prediction　model　has　some　problems,　such　as　poor　stability,　lower　accuracy　and　data　fluctuation.　Obviously,　it　is　necessary　to　establish　a　combination　model　to　accurately　predict　slope　deformation.　Here,　we　used　the　GFW-Fisher　optimal　segmentation　method　to　establish　a　multi-scale　prediction　combination　model.　Our　results　indicated　that　the　determination　coefficient　of　linear　combination　model,　weighted　geometric　average　model,　and　weighted　harmonic　average　model　was　the　highest　at　the　surface　spatial　scale　with　a　large　scale,　and　their　determination　coefficients　were　0.95,　0.95,　and　0.96,　respectively.　Meanwhile,　RMSE,　MAE　and　Relative　error　were　used　as　indicators　to　evaluate　accuracy　and　the　evaluation　accuracy　of　the　weighted　harmonic　average　model　was　the　most　obvious,　with　an　accuracy　of　5.57%,　3.11%　and　3.98%,　respectively.　Therefore,　it　is　necessary　to　choose　the　weighted　harmonic　average　model　at　the　surface　scale　with　a　large　scale　as　the　slope　deformation　prediction　combination　model.　Meanwhile,　our　results　effectively　solve　the　problems　of　the　prediction　results　caused　by　the　single　model　and　data　fluctuation　and　provide　a　reference　for　the　prediction　of　slope　deformation.