The sintering completeness is the intuitive embodiment of the sintering thermal state, and its accurate identification is of great significance to improve the stability and production efficiency of the sintering process and reduce the energy consumption of sintering production. Due to the harsh production environment at the sintering site, the complex combustion reaction, and the frequent convection & thermal exchange between flue gas and materials, it is difficult to accurately identify the sintering completeness in real time. At present, it still relies on "artificial fire watching". In order to solve the existing problems, an intelligent recognition method of sintering completeness based on infrared thermal image features of sintering machine tail is proposed. In this method, firstly, the infrared thermal image is preprocessed, and a two-stage keyframe extraction method is proposed to obtain the keyframe image with little dust interference and complete and clear red layer; and then the ROI area of the sintered red layer is accurately extracted based on the change trend of the R channel value in the RGB attribute of the thermal image, and the hidden layer information is mined based on the image structure texture decomposition to obtain the height of the partitioned layer, and the characteristics such as the proportion distribution of the burning layer height are designed and extracted to characterize the sintering completeness; finally, the DS-CEFNet intelligent recognition model is constructed, which integrates the deep features of the image and the hand-engineered shallow features to achieve accurate recognition of sintering completeness. The results show that the model proposed has an accuracy of 96% for the recognition of sintering completeness, which has good application value.