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The survival quality of these patients depends on the reliable prediction of NPC stages III and IVa. In this paper, we propose a two-stage framework to produce the classification probabilities for predicting NPC stages III and IVa. The preprocessing of MR images enhance the quality of images for further analysis. In stage one transfer learning is used to improve the classification effectiveness and the efficiency of CNN models training with limited images. Then in stage two the output of these models are aggregates using soft voting to boost the final prediction. The experimental results show the preprocessing is quite effective, the performance of transfer learning models perform better than the basic CNN model, and our ensemble model outperforms the single model as well as traditional methods, including the TNM staging system and the Radiomics method. Finally, the prediction accuracy boosted by the framework is, respectively,<jats:bold>0.81<\/jats:bold>, indicating that our method achieves the SOTA effectiveness for LA-NPC stage prediction. 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