In　this　paper,　we　consider　variable　selection　procedure　for　the　high　dimensional　partially　linear　varying　coefficient　models　where　the　parametric　part　covariates　are　measured　with　additive　errors.　The　penalized　bias-corrected　proffile　least　squares　estimators　are　conducted,　and　their　asymptotic　properties　are　also　studied　under　some　regularity　conditions.　The　rate　of　convergence　and　the　asymptotic　normality　of　the　resulting　estimates　are　established.　We　further　demonstrate　that,　with　proper　choices　of　the　penalty　functions　and　the　regularization　parameter,　the　resulting　estimates　perform　asymptotically　as　well　as　an　oracle　property.　Choice　of　smoothing　parameters　is　also　discussed.　Finite　sample　performance　of　the　proposed　variable　selection　procedures　is　assessed　by　Monte　Carlo　simulation　studies.