Seamless steel pipe production, as a typical representative of high-energy-consuming industries, has always been a focus of energy-saving and consumption reduction. By predicting power consumption, enterprises can identify effective ways to save energy, thereby reducing electricity consumption in the production process and improving production efficiency. In order to improve the accuracy of electricity consumption prediction for seamless steel pipe continuous rolling, an improved Stacking ensemble learning model is adopted to predict power consumption. Firstly, the collected power consumption data is preprocessed, and XGBoost and LightGBM are used for feature selection based on embedding method. Then, a combination of random search and Bayesian optimization is used to optimize the hyperparameters of the base learners. In the first layer of the Stacking ensemble model, LightGBM, ET, and MLP are selected as the base learners. Finally, based on the predictive performance of the base learners on the data, they are assigned corresponding weights, and the original dataset is also included in the training of the meta-learner. The results show that the improved Stacking ensemble learning model has the best prediction effect, with an R2 of 0.975 6. The prediction accuracy is higher than that of single base learners and traditional Stacking ensemble learning models, demonstrating the effectiveness of the proposed method.