In　this　paper,　we　focus　on　the　variable　selection　for　the　semiparametric　regression　model　with　longitudinal　data　when　some　covariates　are　measured　with　errors.　A　new　bias-corrected　variable　selection　procedure　is　proposed　based　on　the　combination　of　the　quadratic　inference　functions　and　shrinkage　estimations.　With　appropriate　selection　of　the　tuning　parameters,　we　establish　the　consistency　and　asymptotic　normality　of　the　resulting　estimators.　Extensive　Monte　Carlo　simulation　studies　are　conducted　to　examine　the　finite　sample　performance　of　the　proposed　variable　selection　procedure.　We　further　illustrate　the　proposed　procedure　with　an　application.