Abstract: The basic oxygen furnace (BOF) steelmaking process, as the predominant steelmaking method used around the world, involves very complex physical and chemical phenomena such as multi-component reactions, multi-phase fluid dynamics, and high temperature. The main task of the BOF process is tailoring the temperature and melt components to meet the requirements of high-quality steel production. With the development of intelligent steelmaking, the prediction of the end-point manganese content is an extremely important task for the BOF process, and improving the level of control regarding the end-point of BOF steelmaking can reduce production costs and enhance efficiency. In this paper, the mechanism of the BOF steelmaking process and the factors influencing the endpoint manganese content were analyzed. The control variables for predicting the end-point manganese content were also determined. To solve the problems of slow convergence, weak generalization ability, and low prediction accuracy in the prediction model established for the BP neural network, a new modeling concept based on an extreme learning machine (ELM) algorithm was proposed. By introducing regularization and improved particle swarm optimization (IPSO), a prediction model for the end-point manganese content in a converter based on improved particle swarm optimization and a regularized ELM (IPSO-RELM) was established. The paper then trained and verified the performance of these models with actual production data. A comparison of the performance of the proposed model with those of the prediction model of the BP neural network, the ELM model, and the RELM model reveals that the IPSO-RELM prediction model has the highest prediction accuracy and the best generalization performance. The hit ratio of the IPSO-RELM prediction model is 94%when the predictive errors of the model are within 0. 025%, the mean square error is 2. 18 × 10^-8, and the fitting degree is 0. 72. Relative to the above three models, the IPSO-RELM prediction model may provide a more accurate prediction of the end-point manganese content and thus serves as a good reference point for actual production.