Abstract: Converter steelmaking is an extremely complex physical and chemical reaction process，and the research on prediction model with intelligent method is a hot spot in recent years. Aiming at the end-point hit rate of carbon content and temperature，a new modeling method of static predictionwas proposed. Based on the traditional twin support vector regression，the wavelet weight matrix was introduced into the objective function. Then，the objective function was transformed to the unconstrained optimization problem，which improved the performance andcomputational efficiency of the algorithm.Finally，the datasets of 260t BOF were collected from some steel plant to establish the static prediction model. The experimental results showed that the end point carbon mass fraction (error±0.005%) and temperature (error±10℃) of the prediction model achieved a hit rate of 94% and 96%，respectively，and the double hit rate was 90%. Compared with the existing methods，the proposed prediction model achieved the optimal results，which could not only guide the actual production，but also was used for the mathematical modeling of other application background in metallurgical industry.

Key words: BOF steelmaking, twin support vector regression, wavelet transform, prediction model