Abstract: To address the issue of low thickness control accuracy during non-steady-state rolling processes, such as specification switching and roll change in hot continuous rolling, this paper proposes a data-driven model based on Stacking ensemble learning for thickness prediction during specification switching. The optimal hyperparameter configurations of both the base learners and the meta-learner are determined using a Bayesian optimization algorithm. The performance of the Stacking model is systematically compared with that of other mainstream models. Additionally, the SHAP（SHapley Additive exPlanations） method is introduced to interpret the Stacking model and evaluate the importance of input features. The results demonstrate that the proposed Stacking model effectively integrates the predictions of base learners, significantly enhancing the accuracy of thickness prediction. The performance evaluation metrics of the developed Stacking model are as follows: root mean squared error（RMSE） of 0.019 9, mean absolute error（MAE） of 0.014 9, and coefficient of determination（R²） of 0.999 9. The probability that the error between the predicted and actual thickness is within ±35 μm reaches 94.8%, while the probability within ±50 μm is 97.8%, achieving high-precision thickness control during the specification switching process in hot continuous rolling.