The　explicit　mapping　of　spatial　soil　pH　is　beneficial　to　evaluate　the　effects　of　land-use　changes　in　soil　quality.　Digital　soil　mapping　methods　based　on　machine　learning　have　been　considered　one　effective　way　to　predict　the　spatial　distribution　of　soil　parameters.　However,　selecting　optimal　environmental　variables　with　an　appropriate　feature　selection　method　is　key　work　in　digital　mapping.　In　this　study,　we　evaluated　the　performance　of　the　support　vector　machine　recursive　feature　elimination　(SVM-RFE)　feature　selection　methods　with　four　common　performance　machine　learning　methods　in　predicting　and　mapping　the　spatial　soil　pH　of　one　urban　area　in　Fuzhou,　China.　Thirty　environmental　variables　were　collected　from　the　134　samples　that　covered　the　entire　study　area　for　the　SVM-RFE　feature　selection.　The　results　identified　the　five　most　critical　environmental　variables　for　soil　pH　value:　mean　annual　temperature　(MAT),　slope,　Topographic　Wetness　Index　(TWI),　modified　soil-adjusted　vegetation　index　(MSAVI),　and　Band5.　Further,　the　SVM-RFE　feature　selection　algorithm　could　effectively　improve　the　model　accuracy,　and　the　extreme　gradient　boosting　(XGBoost)　model　after　SVM-RFE　feature　selection　had　the　best　prediction　results　(R-2　=　0.68,　MAE　=　0.16,　RMSE　=　0.26).　This　paper　combines　the　RFE-SVM　feature　selection　with　machine　learning　models　to　enable　the　fast　and　inexpensive　mapping　of　soil　pH,　providing　new　ideas　for　predicting　soil　pH　at　small　and　medium　scales,　which　will　help　with　soil　conservation　and　management　in　the　region.