The water absorption profile can reflect the difference in the waterflooding status of each sublayer in the injection well, and predicting the water absorption profile has important guiding significance for formulating and adjusting the waterflood development plan. Currently, numerical simulation and radioisotope logging techniques are commonly used to obtain the water absorption in sublayers. However, the complex and increasingly heterogeneous subsurface formations in China's oilfields have made it impossible for the numerical simulation to reflect the actual reservoir water injection status by splitting the water absorption into sublayers. Although the radioisotope method is more accurate, it is more expensive and has less actual test data in oilfields. In addition, the water absorption prediction model built by traditional machine learning only applies to injection wells with a small amount of water absorption profile data, and its prediction accuracy needs further improvement. In this paper, a new machine learning prediction method is proposed to build a water absorption profile prediction model by stacking ensemble learning algorithm, which achieves the prediction of water absorption in sublayers for injection wells without historical data of water absorption profiles during the development period. In our model, based on the oilfield's static geological data and dynamic production data, the input feature parameters are comprehensively selected by a hybrid feature selection method to analyze the importance of each feature. The robust base-learners are extracted from the sub-model pool by comparing and analyzing the comprehensive prediction performance of each individual model, including ANN, KNN, SVR, RF, XGBoost, LightGBM, etc. The stacking ensemble model was trained using K-fold cross-validation on a randomly divided dataset, which improves the predictive performance of an individual model by combining the advantages of multiple base-learners so that it can accurately reflect the dynamic changes of the water absorption profile. Finally, a case study from the MG study area of the DG oilfield was conducted to verify the model's predictive effect. This case shows that the stacking model proposed in this paper has better performance in predicting the water absorption in sublayers compared with the traditional single model and other ensemble models, and the model has a strong generalization ability, which proves that the method can be used in studying the prediction of the water absorption in sublayers of injection wells.