Abstract: Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data, whose manual annotation is both labor-intensive and cost-prohibitive. To mitigate this challenge, we present an enhanced semi-supervised learning approach based on the Mean Teacher framework, incorporating a novel feature loss module to maximize classification performance with limited labeled samples. The model studies show that the proposed model surpasses both the baseline Mean Teacher model and fully supervised method in accuracy. Specifically, for datasets with 20%, 30%, and 40% label ratios, using a single training iteration, the model yields accuracies of 78.61%, 82.21%, and 85.2%, respectively, while multiple-cycle training iterations achieves 82.09%, 81.97%, and 81.59%, respectively. Furthermore, scenario-specific training schemes are introduced to support diverse deployment need. These findings highlight the potential of the proposed technique in minimizing labeling requirements and advancing intelligent blast furnace diagnostics.