In industrial soft-sensor applications, labeled samples are often scarce and unable to fully represent the dynamic changes in industrial processes. Although semi-supervised methods offer a potential solution to this issue, existing feature-construction-based methods cannot ensure the effectiveness of the feature, and pseudo-label-based methods lack an established confidence evaluation standard. To address these challenges, this article first proposes a novel performance-driven distillation strategy, which designs an innovative siameseLSTM structure for training multiple teacher models. By assigning higher weights to high-performance teacher models and simultaneously leveraging the guidance of the soft sensing task, the student model is guided to learn more effective feature representations. Additionally, a new pseudo label confidence evaluation strategy is introduced, which aims to enhance the generalization of the base soft-sensor model by selecting samples with high-confidence pseudo labels. Finally, By combining the above two strategies, a semi-supervised soft-sensor framework is proposed for the soft sensing of industrial quality variables. The effectiveness of the proposed framework is validated through two real-world datasets from different stages of the alumina production process. Compared with some existing advanced soft sensor frameworks, the prediction results on different datasets show that the root-mean-square error (RMSE) and mean absolute error (MAE) are reduced by an average of 10.76% and 11.18%, respectively, while the correlation coefficient (R2) is averagely increased by 0.1203.