A　nonconcave　penalized　estimation　method　is　proposed　for　partially　linear　models　with　longitudinal　data　when　the　number　of　parameters　diverges　with　the　sample　size.　The　proposed　procedure　can　simultaneously　estimate　the　parameters　and　select　the　important　variables.　Under　some　regularity　conditions,　the　rate　of　convergence　and　asymptotic　normality　of　the　resulting　estimators　are　established.　In　addition,　an　iterative　algorithm　is　proposed　to　implement　the　proposed　estimators.　To　improve　efficiency　for　regression　coefficients,　the　estimation　of　the　covariance　function　is　integrated　in　the　iterative　algorithm.　Simulation　studies　are　carried　out　to　demonstrate　that　the　proposed　method　performs　well,　and　a　real　data　example　is　analysed　to　illustrate　the　proposed　procedure.