Classification of silicon content variation trend based on fusion of multilevel features in blast furnace ironmaking

•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.