The ensemble learning models such as Light Gradient Boosting Machine (LightGBM) only mine data information once, and cannot automatically refine the granularity of data mining or obtain more potential internal correlation information in the data by deep digging. In order to solve the problems, a deep LightGBM ensemble learning model was proposed, which was composed of sliding window and deepening. Firstly, the ensemble learning model was able to automatically refine the granularity of data mining through the sliding window, so as to further mine the potential internal correlation information in the data and a certain expressive learning ability was given to the model. Secondly, based on the sliding window, the deepening step was used to further improve the representation learning ability of the model. Finally, the dataset was processed with feature engineering. The experimental results on the dataset of Google store show that, the prediction accuracy of the proposed deep ensemble learning model is 6.16 percentage points higher than that of original ensemble learning model. The proposed method can automatically refine the granularity of data mining, so as to obtain more potential information in the dataset. Moreover, compared with the traditional deep neural network, the deep LightGBM ensemble learning model has fewer parameters and better interpretability as a non-neural network.