The operation of a blast furnace system involves strong multiscale features to which enough attention should be paid, when describing its complex dynamics. For this purpose, a data-based multiscale modeling algorithm that can extract and pick out some subscale variables most relevant to the output from the original input variables set is presented. These selected subscale variables acting as inputs together with the output are introduced into a general linear or nonlinear model framework to form the corresponding multiscale model. Through model validation, the constructed multiscale models are found to exhibit large advantage compared with those traditional models based on the averaging idea over a fixed scale, especially in the cases of nonlinear models, in which high agreement between the predicted values and the real ones is observed. These results indicate that the proposed multiscale modeling algorithm on the one hand, can provide a kind of thought to develop a data-based multiscale model from the viewpoint of methodology, and conversely, can serve as a potential blast furnace modeling tool from the viewpoint of engineering applications. (c) 2014 American Institute of Chemical Engineers AIChE J, 60: 2197-2210, 2014