In order to make statistical analysis effective feature screening has been widely studied by many scholars in the ultra-high dimensional field. Aiming at the problem that the existing feature screening methods cannot flexibly deal with the intra-group correlation of ultra-high dimensional longitudinal data an iterative feature screening method based on dynamic covariance modeling was proposed. This method is called the iterative dynamic feature screening method. At each iteration the modified Cholesky decomposition was used to replace the static covariance matrix modeling method to dynamically model the intra-group covariance matrices of longitudinal data to obtain the flexible estimators of them and then these estimators were substituted into the generalized estimating equation GEE to establish the feature screening criteria for screening according to the idea of GEE-based screening procedure GEES . Finally the final submodel was obtained when the iterative algorithm converged. Random simulations and yeast cell-cycle gene expression dataset were introduced to test the iterative dynamic feature screening method GEES and the other two classical independent feature screening methods. The results show that the iterative dynamic feature screening method can quickly screen out important covariates can deal with the intra-group correlation of longitudinal data more flexibly and has higher screening accuracy.