A novel iteration method is designed based on a hybrid augmented Kalman filter (HAKF) algorithm and linear global-state model of the flexible rotor-bearing system to identify the dynamic coefficients of journal bearings. Compared with the related methods, this method only requires the unbalance response of the two locations, while not limited to the bearing locations. To improve the efficiency of the proposed method, equivalent oil-film force is posted as the stochastic random variables in the established global-state model and then is predicted using the unbalance responses of the system. According to the relationship between equivalent oil-film force and system parameters, the bearing coefficients are estimated by the weight residual method. The numerical studies and experiment identifications present that the proposed method has a satisfying capability for dynamic characteristics identification and more convenient than the conventional method.