Abstract: Aiming at the problem that the single model classification algorithm has a low success rate when the number of training samples is low, an ensemble learning algorithm is presented in this paper. The experiment was conducted by applying DPA_Contest_V4 dataset. First the traditional method is used to break the mask, and then SVM, RF and kNN classification algorithms are applied to train and predict. Finally, the results of these models are combined as an ensemble model. The experimental results show that the integrated model is superior to the single model, and the success rate of the ensemble model can be about 10% higher than that of the single model especially when the number of training samples is low.