In　this　article,　we　study　the　statistical　inference　for　the　generalized　partially　linear　model　with　random　effect.　We　develop　the　traditional　models　that　can　model　generalized　longitudinal　data　and　treat　categorical　data　as　continuous　data　by　using　some　transformations.　We　propose　a　class　of　semiparametric　estimators　for　the　parametric　and　variance　components.　The　proposed　estimators　are　data　adaptive,　which　does　not　require　any　assumption　of　working　likelihood　for　the　random　component　or　model　error.　We　prove　that　the　resulting　estimators　for　the　parametric　component　are　consistent　and　asymptotic　normal,　but　also　remain　semiparametrically　efficient.　The　asymptotic　normality　is　established　for　the　proposed　estimator　of　variance　component.　Moreover,　we　also　propose　an　estimator　for　the　nonparametric　component　by　using　the　local　linear　smoother　and　present　their　asymptotic　normality.　Finite　sample　performance　of　the　proposed　procedures　is　evaluated　by　Monte　Carlo　simulation　studies.　We　further　illustrate　the　proposed　procedure　by　an　application.