For the effective use of the valid information contained in the flame image of the tail section of the sintering machine, the random forest algorithm is used to predict the sintering state in a short time. The algorithm is feasible in engineering. To improve the influence of low-importance properties in random forests on classification results, we propose a random forest improvement algorithm using probability decision, making to realize the short-term prediction of the flame state of the sintering machine tail section. First, 300 sintered section flame images were uniformly preprocessed, and geometric features of 10 images were given as input. Second, K-mean and fuzzy C-mean clusterings were performed on the geometric features of the 10 extracted images. Finally, the probability of each category appearing at the leaf node was given according to the accuracy of the clustering results. The experiment proves that the proposed optimized random forest algorithm improves the accuracy for sintering state classification.