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• A data-driven model is proposed based on multilevel features fusion to classify the variation trend of silicon content. • Multilevel features are extracted through variable analysis, statistical information, and stacked denoising autoencoder, respectively. • Silicon content trend labels are quantitative descripted by seven different primitives. • Both experimental simulation and industrial application verify the effectiveness and feasibility of the proposed model. The silicon content variation trend, which can reflect the quality of molten iron, provides significant information that can assist in ensuring the smooth operation of a blast furnace. This paper proposes a novel dynamic data-driven model for the online classification of the variation trend for the silicon content. Typically, a dynamic model for the silicon content variation trend primarily relies on process data feature extraction. First, a multilevel features fusion algorithm based on mutual information is developed to extract a rich and robust feature representation. Subsequently, the fused multilevel feature vectors and their corresponding trend labels are fed into a recurrent neural network model to capture the process dynamics and classify the variation trend. An experimental simulation and industrial application verified the effectiveness and feasibility of the proposed method. The classification results can provide guidance to ensure that the quality of molten iron is maintained within the desired range in the ironmaking process. [ABSTRACT FROM AUTHOR]