We　consider　the　problem　of　variable　selection　in　high-dimensional　partially　linear　models　with　longitudinal　data.　A　variable　selection　procedure　is　proposed　based　on　the　smooth-threshold　generalized　estimating　equation　(SGEE).　The　proposed　procedure　automatically　eliminates　inactive　predictors　by　setting　the　corresponding　parameters　to　be　zero,　and　simultaneously　estimates　the　nonzero　regression　coefficients　by　solving　the　SGEE.　We　establish　the　asymptotic　properties　in　a　high-dimensional　framework　where　the　number　of　covariates　p(n)　increases　as　the　number　of　clusters　n　increases.　Extensive　Monte　Carlo　simulation　studies　are　conducted　to　examine　the　finite　sample　performance　of　the　proposed　variable　selection　procedure.