The importance measure analysis can find out the important feature variables of model, which can effectively reduce the variable dimension and decrease the computation time. The relationship between the important measure of random forest and the variance-based global sensitivity measure was explored, which can give a novel way to solve variance-based main sensitivity index S_i and total sensitivity index S^T_i. The importance measure of single and group variables based on random forest were established to improve the corresponding measure index system. Several examples are given to verify the validity of the proposed important measures and the correctness relation derivation about variance-based sensitivity indices.