In order to improve the bad robustness and morbidity of parameter identification of GM(1,1) model, on one hand, the ill-conditioned matrix of parameter identification is transformed into the good-conditioned matrix by using the multiply transformation, which does not change the prediction precision of the model; on the other hand, the process of parameter identification is transformed into solving the linear equations with the recursive form by using the appropriating orthogonal matrix. By using the proposed methods, the morbidity problem of least squares is avoided, and the applicability of the model
is improved. The numerical example and Monte-Carlo simulation show the effectiveness of the proposed methods.