Basic oxygen furnace (BOF) steelmaking plays an important role in the steelmaking process, so it is very important to control the converter process accurately. The control process of BOF steelmaking is based on the end point prediction of BOF. Therefore, in order to achieve accurate steelmaking, it is necessary to establish the end point prediction model of BOF steelmaking. According to the characteristics of BOF end-point prediction, an improved fuzzy support vector regression (IFSVT) algorithm is proposed in this paper. IFSVR is built by introducing the parameter based on fuzzy support vector regression (FSVR). In addition, in order to improve the optimization efficiency, particle swarm optimization (PSO) algorithm is used in the parameter optimization of IFSVR, so as to improve the modeling speed. The experimental results show that the proposed models are effective and feasible. Within different error bounds (0.005% for carbon content model and 10 C for temperature model), the hit rates of carbon content and temperature realize 91% and 94%, respectively. And a double hit rate of 90% is obtained, which can provide a significant reference for real BOF applications, and the proposed method is also appropriate for the prediction models of other metallurgical applications.