保护渣的黏度对铸坯表面和内部的质量有直接的影响。为准确预测保护渣黏度值，辅助保护渣的成分设计，提出了一种基于软注意力的门控循环单元（gate recurrent unit, GRU）模型的黏度预测算法。首先，收集无氟保护渣成分数据集，采用皮尔逊相关性分析方法对保护渣中的各个成分与黏度值进行相关性分析；然后将软注意力机制融入GRU模型的输入处理阶段，增强输入特征，提升对全局特征信息的捕捉；最后利用无氟保护渣数据集进行黏度预测实验，将优化算法与经典预测算法进行对比，验证融合软注意力机制的GRU模型的有效性。试验结果表明，在相同超参数的条件下，该模型的平均绝对百分比误差为7.78%、决定系数为0.906 5、均方误差为0.003 3，均高于对比模型，在预测精度与稳定性上取得较大提升，更适用于无氟保护渣黏度的预测。

The viscosity of the protective slag has a direct influence on the surface and internal quality of the cast billet. To precisely predict the viscosity value of the protective slag and assist in the composition design of the protective slag, a viscosity prediction algorithm based on a gated recurrent unit (GRU) model with soft attention was put forward. Firstly, a dataset of fluoride-free protective slag composition was collected, and the Pearson correlation analysis method was employed to conduct a correlation analysis between each component in the protective slag and the viscosity value. Then, the soft attention mechanism was integrated into the input processing stage of the GRU model to enhance the input features and improve the capture of global feature information. Finally, viscosity prediction experiments were carried out using the fluoride-free protective slag dataset, and the optimized algorithm was compared with the classical prediction algorithms to verify the validity of the GRU model integrated with the soft attention mechanism. The experimental results demonstrate that under the same hyperparameters, the average absolute percentage error of this model is 7.78%, the coefficient of determination is 0.906 5, and the mean square error is 0.003 3, all of which are higher than those of the comparison models. The prediction accuracy and stability have been significantly enhanced, making it more applicable for the prediction of the viscosity of fluoride-free protective slag.