In　this　paper,　we　study　ultra-high-dimensional　partially　linear　models　when　the　dimension　of　the　linear　predictors　grows　exponentially　with　the　sample　size.　For　the　variable　screening,　we　propose　a　sequential　profile　Lasso　method　(SPLasso)　and　show　that　it　possesses　the　screening　property.　SPLasso　can　also　detect　all　relevant　predictors　with　probability　tending　to　one,　no　matter　whether　the　ultra-high　models　involve　both　parametric　and　nonparametric　parts.　To　select　the　best　subset　among　the　models　generated　by　SPLasso,　we　propose　an　extended　Bayesian　information　criterion　(EBIC)　for　choosing　the　final　model.　We　also　conduct　simulation　studies　and　apply　a　real　data　example　to　assess　the　performance　of　the　proposed　method　and　compare　with　the　existing　method.　©　2017,　©　East　China　Normal　University　2017.