Accurate extraction of flame image features for converter steelmaking is the key to predicting end point carbon content. Aiming at the high similarity of flame images, it is difficult to distinguish flame images with similar carbon content, which leads to the problem that the carbon content cannot be accurately predicted. In this paper, an improved complete local binary pattern (ICLBP) color texture feature extraction method is proposed to extract more differentiated flame features at furnace mouth under different carbon contents and predict the endpoint carbon content. Firstly, local phase quantization ( LPQ) is used to extract image frequency domain phase information under different color channels, and the fusion feature ICLBP _ MP is combined with image spatial domain amplitude information extracted by CLBP to enhance the robustness of CLBP algorithm structure. Then, it is weighted by an improved color information weighting strategy to enhance the color contrast information of the flame image. Finally, the K nearest neighbor regression model is used to predict the carbon content. The experimental results show that the accuracy rate of carbon content prediction is 83. 9% within the error range of 0. 02%.