针对翼型结冰预测中单任务学习精度有限且难以同时获得结冰形态和气动特性的问题，提出了一种基于多任务学习的翼型结冰预测框架MTAF(Multi Task Airfoil Former)。该框架采用编码器-解码器架构，通过共享特征编码器提取翼型几何与飞行条件的深层表征，利用任务关联模块实现特征融合与任务交互，由专门的预测头分别完成结冰预测和气动系数预测。对比研究了Transformer和ConvNeXt两种骨干网络在该框架中的性能表现。基于625个NACA 2412翼型样本进行训练和测试，结果表明Transformer架构在结冰预测任务中达到99.79%的像素准确率和0.9906的决定系数，优于ConvNeXt的99.50%和0.9772；在气动系数预测方面，Transformer的升力系数决定系数达到0.9754，阻力系数决定系数达到0.9874，与ConvNeXt的0.9733和0.9901相当，两种架构各有优势。在此基础上，进一步利用100组未参与训练的外部样本进行了泛化性验证，从像素和几何两个层面对比分析表明，MTAF 框架在不同结冰类型和多种飞行工况下均保持较高精度和良好泛化能力，为翼型结冰工程应用提供了高效可靠的预测方法。

To address the problem of limited accuracy in single-task learning for airfoil icing prediction and the difficulty in simultane-ously obtaining icing morphology and aerodynamic characteristics, a multi-task learning based airfoil icing prediction framework called MTAF (Multi-Task Airfoil Former) is proposed. The framework employs an encoder-decoder architecture that extracts deep representations of airfoil geometry and flight conditions through a shared feature encoder, realizes feature fusion and task interaction via a task correlation module, and accomplishes icing prediction and aerodynamic coefficient prediction respectively through dedicated prediction heads. The performance of Transformer and ConvNeXt backbone net-works within this framework is comparatively studied. Based on training and testing with 625 NACA 2412 airfoil samples, results demonstrate that the Transformer architecture achieves 99.79% pixel accuracy and 0.9906 coefficient of determination in icing prediction tasks, significantly outperforming ConvNeXt's 99.50% and 0.9772. For aerodynamic coefficient predic-tion, Transformer achieves coefficient of determination values of 0.9754 for lift coefficient and 0.9874 for drag coefficient, which are comparable to ConvNeXt's 0.9733 and 0.9901 respectively, with each architecture showing distinct advantages. In addition, generalization was evaluated using 100 external cases excluded from training. The results show that the proposed MTAF framework delivers high prediction accuracy and robust generalization across a wide range of icing types and flight conditions at both pixel and geometric levels,providing a high-precision prediction method for airfoil icing engineering ap-plications.