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A mixed bat optimization algorithm based on chaos and differential evolution (CDEBA) is proposed for the endblow process of basic oxygen furnance (BOF) after sub-lance detection, and a prediction model based on BP neural network optimized by chaotic differential bat algorithm (CDEBA-NN) is presented. The simulation results show that the prediction model of carbon content achieves a hit rate of 94 % with the error range of 0,005 %, and 90 % for temperature with the error range of 15 °C, the accuracy is higher than the traditional neural network model, and then it verifies the effectiveness of the proposed model.