Semiparametric　generalized　varying　coefficient　partially　linear　models　with　longitudinal　data　arise　in　contemporary　biology,　medicine,　and　life　science.　In　this　paper,　we　consider　a　variable　selection　procedure　based　on　the　combination　of　the　basis　function　approximations　and　quadratic　inference　functions　with　SCAD　penalty.　The　proposed　procedure　simultaneously　selects　significant　variables　in　the　parametric　components　and　the　nonparametric　components.　With　appropriate　selection　of　the　tuning　parameters,　we　establish　the　consistency,　sparsity,　and　asymptotic　normality　of　the　resulting　estimators.　The　finite　sample　performance　of　the　proposed　methods　is　evaluated　through　extensive　simulation　studies　and　a　real　data　analysis.