Ensemble　learning　synthesizes　the　advantages　of　different　models　and　has　been　widely　applied　in　the　field　of　spatial　prediction.　However,　the　nonlinear　constraints　of　spatial　heterogeneity　on　the　model　ensemble　process　make　it　difficult　to　adaptively　determine　the　ensemble　weights,　greatly　limiting　the　predictive　ability　of　the　ensemble　learning　model.　This　paper　therefore　proposes　a　novel　geographical　spatial　heterogeneous　ensemble　learning　method　(GSH-EL).　Firstly,　the　geographically　weighted　regression　model,　geographically　optimal　similarity　model,　and　random　forest　model　are　used　as　three　base　learners　to　express　local　spatial　heterogeneity,　global　feature　correlation,　and　nonlinear　relationship　of　geographic　elements,　respectively.　Then,　a　spatially　weighted　ensemble　neural　network　module　(SWENN)　of　GSH-EL　is　proposed　to　express　spatial　heterogeneity　by　exploring　the　complex　nonlinear　relationship　between　the　spatial　proximity　and　ensemble　weights.　Finally,　the　outputs　of　the　three　base　learners　are　combined　with　the　spatial　heterogeneous　ensemble　weights　from　SWENN　to　obtain　the　spatial　prediction　results.　The　proposed　method　is　validated　on　the　PM2.5　air　quality　and　landslide　dataset　in　China,　both　of　which　obtain　more　accurate　prediction　results　than　the　existing　ensemble　learning　strategies.　The　results　confirm　the　need　to　accurately　express　spatial　heterogeneity　in　the　model　ensemble　process.