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The converter smelting process contains complex multi-phase and high-temperature physical and chemical reac tions， and it is of great significance to establish a reliable converter endpoint prediction model to effectively reduce the fluc tuation of molten steel composition and improve the quality of steel. Based on the actual production data of a 200 t con⁃ verter in a steel mill， the particle swarm optimization algorithm is used to select the optimal penalty parameter C and ker nel parameter g of the support vector machine model to establish a prediction model， and the carbon mass fraction and tem perature at the end point of the converter are predicted. After data processing 425 sets of data were obtained and divided into training set data and test set data， and normalized them， of which 50 groups were randomly selected as test set data. The results show that the accuracy of carbon mass fraction （error ±0. 015%） and temperature （error ±15 ℃） is 81. 8% and 80% respectively. Compared with BP neural network model and RBF model， support vector machine model optimized by particle swarm optimization has higher accuracy and better generalization ability.