We study a semiparametric generalized additive coefficient model,in which linear predictors in the conventional generalized linear models is generalized to unknown functions depending on certain covariates,and approximate the nonparametric functions by using polynomial spline.The asymptotic expansion with optimal rates of convergence for the estimators of the nonparametric part is established.Semiparametric generalized likelihood ratio test is also proposed to check if a nonparametric coefficient can be simplified as a parametric one.