In　this　paper,　we　simultaneously　study　variable　selection　and　estimation　problems　for　sparse　ultra-high　dimensional　partially　linear　varying　coefficient　models,　where　the　number　of　variables　in　linear　part　can　grow　much　faster　than　the　sample　size　while　many　coefficients　are　zeros　and　the　dimension　of　nonparametric　part　is　fixed.　We　apply　the　B-spline　basis　to　approximate　each　coefficient　function.　First,　we　demonstrate　the　convergence　rates　as　well　as　asymptotic　normality　of　the　linear　coefficients　for　the　oracle　estimator　when　the　nonzero　components　are　known　in　advance.　Then,　we　propose　a　nonconvex　penalized　estimator　and　derive　its　oracle　property　under　mild　conditions.　Furthermore,　we　address　issues　of　numerical　implementation　and　of　data　adaptive　choice　of　the　tuning　parameters.　Some　Monte　Carlo　simulations　and　an　application　to　a　breast　cancer　data　set　are　provided　to　corroborate　our　theoretical　findings　in　finite　samples.