In order to improve the intelligent level of blast furnace production, a prediction model of pressure difference in low part of blast furnace with integrated learning algorithm is proposed, which solves the problem of accurately predicting the lower pressure difference based on online data. Through systematic analysis of the internal mechanism of the blast furnace, the raw material parameters, operating parameters, state parameters and index parameters of the blast furnace are comprehensively selected as the input of the model. The actual field data is used to obtain the correlation coefficient between the variables, and the important characteristic variables related to the pressure difference in the lower part of the blast furnace are determined. The extra tree ensemble algorithm is used to establish the pressure difference prediction model, and combined with the prediction accuracy of the model, the forward selection method is used to optimize the input of the model. By selecting the hyperparameters of the model algorithm, the optimal hyperparameter set is obtained. The accuracy R2 of the lower pressure difference prediction model established by the parameter set reaches 0.8264, and the MSE is close to zero. The test results prove that the model has good prediction accuracy and generalization ability, and has important guiding significance for the on-site operators to predict the operating conditions of the blast furnace and adjust the furnace conditions in advance.