在转炉炼钢过程中，钢液成分的准确判定是出钢的关键环节。 目前主要是依靠生产经验判断是否到达冶炼终点，同时对钢液取样分析。 这种方式不仅限制了生产效率，还受到了工人经验的影响。 为减少人为经验的影响，提出了一种基于灰狼优化算法和重要特征改进的随机森林模型。 以某钢厂 120 t 转炉为研究对象，选取铁液质量、废钢比例、吹炼时间、铁液中 Si、Mn、P 含量、铁液温度、转炉操作参数以及氧气、氩气、氮气消耗量等多维特征作为输入变量，实现了对终点钢液中 C、Si、Mn、P、S 等元素含量的实时预测。 基于 1783 组实际工艺数据对模型进行了训练与动态修正，通过超参数调优将预测时间缩短至 0.1~0.3 s，预测准确率超过了 90%。 模型在提升泛化能力与预测稳定性的同时，实现了钢液成分的快速预报，有效降低了人为干预对终点判断的影响。

In the basic oxygen furnace (BOF) steelmaking process, accurate determination of the molten steel composition is a critical step in determining the tapping point. Currently, this decision relies primarily on operator experience, supplemented by manual sampling and laboratory analysis. However, such an approach not only limits production efficiency but is also subject to human error. To reduce the influence of subjective judgment, an improved random forest (RF) model optimized by the grey wolf optimization (GWO) algorithm was proposed. Using a 120-ton converter at a steel plant as the research object, multiple process parameters were selected as input features, including the hot metal weight, scrap ratio, blowing time, Si, Mn and P contents of the hot metal, hot metal temperature, the converter operation parameters, and the consumption of oxygen, argon, and nitrogen. The model enables real-time prediction of the endpoint concentrations of C, Si, Mn, P and S in molten steel. The model was trained and dynamically updated via 1 783 sets of actual industrial data. Through hyperparameter tuning, the prediction time is reduced to 0.1~0.3 s, with a prediction accuracy exceeding 90%. While improving generalizability and stability, the model achieves fast and reliable prediction of steel composition and significantly reduces dependence on manual decision-making.