In　this　paper,　the　partially　linear　varying　coefficient　models　were　established　when　the　predictors　of　the　linear　part　were　ultra-high　dimensional,　where　the　dimensionality　grew　exponentially　with　the　sample　size.　A　greedy　profile　forward　regression　(GPFR)　method　was　proposed　to　finish　the　variable　screening　for　the　ultra-high　dimensional　linear　predictors.　Under　some　regularity　conditions,　the　proposed　GPFR　method　has　a　screening　consistency　property　was　proven.　As　for　the　GPFR　procedure　obtaining　a　list　of　the　nested　models,　to　determine　whether　or　not　to　include　the　candidate　predictor　in　the　model　of　selected　ones,　an　extended　Bayesian　information　criterion　(EBIC)　was　adopted　to　select　the　＂best＂　candidate　model.　The　finite-sample　performance　of　the　proposed　GPFR　method　was　assessed　by　using　simulation　studies　and　real　data　analysis.　The　result　shows　that　the　proposed　GPFR　method　has　advantage　in　the　cases　existing　high　correlation　between　the　predictors　and　low　signal　noise　ratio.　©　2018,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.