传统转炉供氧量预测模型多从机理或算法单一角度入手，忽视转炉冶炼数据的高噪音性和模型训练的偶然性，导致实用性和可信度不足。为此，本工作提出一种基于多网络优化的混合预测模型，首先采用孤立森林算法剔除异常值，之后结合弹性网络与BP神经网络进行建模，最后通过五折交叉验证提高模型泛化能力、网格搜索确保全局最优解。以国内150 t氧气转炉为研究对象验证模型使用效果，结果表明，在±200和±300 Nm3误差范围内，预测命中率分别为76.54%和94.61%；模型的R2=0.6512，RMSE=159.7 Nm3，最大绝对误差(MAE)≤350 Nm3。此外，研究表明融合多种数据分析方法能够提高模型的泛化能力和置信度，MAE是衡量模型可用性的重要指标。

The converter steelmaking process is a crucial stage in iron and steel production, where effective control of oxygen supply significantly impacts the stability of the smelting process and the quality of molten steel. Traditional oxygen supply prediction models often focus on either mechanistic or algorithmic aspects but tend to overlook the high noise levels in the converter environment and the randomness in model training, leading to limitations in their practicality and reliability. To address these challenges, this study proposes a hybrid model based on multi-network optimization for predicting oxygen supply in converters. The model first applies the isolation forest algorithm to remove outliers, and then constructs a hybrid prediction model by combining elastic net with a backpropagation (BP) neural network. Five-fold cross-validation improves the model's generalization ability, and grid search ensures a globally optimal solution. The model is validated on data from a 150-ton oxygen converter in an industrial case study, and its performance is compared with three other models. Results show that the proposed model achieve a prediction hit rate of 76.54% within a ±200 Nm3 error range, and 94.61% within a ±300 Nm3 error range, with an R2 of 0.6512, RMSE of 159.7 Nm3, and MAE≤350 Nm3. This study demonstrates that integrating multiple network optimization methods can significantly improve prediction accuracy and model stability, highlighting the importance of MAE as a key metric for model usability.