The flame image of the sintering machine tail section contains many features related to the sintering endpoint. It is feasible and practical to make full use of the feature information of the sintering flame image to judge the state of the sintering endpoint online. Aiming at the problems of difficult extraction of flame image feature information of sintering machine tail section, low recognition accuracy, and difficulty meeting realtime requirements, an improved MobileNetV3 sintering section flame image recognition algorithm is proposed. MobileNetV3 is taken as the basic model for feature extraction of flame state at the sintering endpoint, and the attention mechanism is introduced; it improves the attention structure of the SE channel to solve the problem of weak resolution of features extracted from the original model; The introduction of Spatial Attention (SA) mechanism and the design of Two Branch Channel Spatial Attention (TBCSA) module accurately capture the position and content information of the red fire zone in the flame image of the sintering section; The data enhancement and cosine annealing learning rate are introduced to improve the generalization ability of the model, and the freezing training strategy is used to accelerate the model convergence. The experiment on the sintering flame data set shows that the algorithm can fully use the feature information in the sintering flame image, and the recognition accuracy reaches 97.54%, which is 6.41 percentage points higher than before.