采用数值模拟与深度学习相结合的方法，以钢锭凝固过程中的缩孔疏松缺陷为研究对象，提出了一种基于Transformer 神经网络与注意力机制的缩孔疏松预测模型。 对 8t 9Cr3Mo 钢锭凝固过程进行有限元数值模拟，获得节点温度、固相率等时序特征数据；将模拟数据作为输入，构建多头自注意力机制的 Transformer 回归模型，实现缩孔疏松预测 ；最后 ，通过 分 析 模 型 注 意 力 权 重 ，揭示 其 对 凝 固 过 程 不 同 阶 段 固 相 率 等 关 键 特 征 的 关 注 规 律 。 结果 表 明 ，该模型在 预 测缩孔疏松时能自动聚焦钢锭凝固后期阶段，与缩孔形成的物理机制一致，为识别缩孔敏感区提供了数据驱动的新视角。

Acombined approach of numerical simulation and deep learning was proposed to investigate shrinkage porosity defects during the solidification process of steel ingots. A Transformer neural network model with attention mechanisms was developed for predicting shrinkage porosity. First, finite element numerical simulations were conducted for the solidification process of an 8-ton 9Cr3Mo steel ingot to obtain time series data such as the nodal temperature and solid fraction. The simulation data were subsequently used as input to construct a Transformer regression model with multi-head self-attention mechanisms to predict shrinkage porosity. Finally, the attention weights of the model were analysed to reveal its focus on key features such as the solid fraction at different stages of the solidification process. The results show that the model automatically focuses on the late solidification stage of the steel ingot, which aligns with the physical mechanism of shrinkage formation, providing a data-driven perspective for identifying shrinkage-prone regions.