The converter smelting process is characterized by multivariable, nonlinear, and strongly coupled dynamics, where oxygen blowing control significantly influences the composition and temperature of molten steel. To achieve precise forecasting of the oxygen-blowing volume, actual production data were first preprocessed via the boxplot method. Subsequently, prediction models for converter oxygen consumption were constructed on the basis of the back propagation neural network (BP) algorithm and the extreme learning machine (ELM) algorithm. The Bayesian optimization (BO) algorithm was employed to optimize the hyperparameters of the BP neural network algorithm and ELM algorithm. Finally, model performance was evaluated via multiple metrics. The results demonstrate that the BO-ELM prediction model outperforms the BO-BP model, achieving R2,RMSE, and MAE values of 0.721, 137.176,and 113.622,respectively. The hit ratio within the error range of ±300 m3 of oxygen consumption was 98.10%.