The variable selection problem of a partially linear model is considered when the number of the parameters diverges for longitudinal data. The nonparametric components are first eliminated by B-spline and QR decomposition, and the penalized objective function of the regression coefficient in the partial linear model is constructed by combining SCAD penalty and quadratic inference function, the variable selection and estimation of regression coefficients are obtained at the same time. Further, the estimates of the nonparametric components are obtained by combination with the quadratic inference function. The asymptotic properties of the regression coefficient estimates and nonparametric component estimates are demonstrated under some regular conditions. The simulation results show that the proposed method has a good estimation effect regardless of whether the correlation structure of longitudinal data is specified correctly. Finally, the method proposed is used to analyze the factors affecting the operating performance of real estate listed companies.