The end-point temperature is one of parameters for the end-point control in the converter. Accurate prediction of the end-point temperature is helpful to improve the hit rate of the end-point. An improved CBR model using time-series data (CBR_TM) was proposed to predict the end-point carbon content and temperature in the converter according to the data types of process parameters. The attributes of the cases in the model not only include the influencing factors of single-value type such as composition and temperature of hot metal, but also include the influencing factors of time-series type such as lance position and oxygen flow, in the case retrieval process, the single-value data similarity and time-series data similarity between the cases were calculated based on the Euclidean distance and the dynamic time warping algorithm, and then weighted to obtain the comprehensive similarity. Then the influence of the weight of the time-series data similarity on the prediction accuracy was studied based on the production data. Finally, the prediction accuracy of the established model was also compared to models based on SVR and BPNN. The results show that: The prediction accuracy of the model increases at first and then decreases with the increase of similarity weight of time series data. The prediction accuracy of the model was the highest when the weight of time-series data similarity was 0.4 and was better than the SVR and BPNN models. The established can meet the requirements of field production.

终点温度是转炉中终点控制的参数之一。准确预测终点温度有助于提高终点的命中率。提出了一种使用时间序列数据（CBR_TM）的改进CBR模型，根据工艺参数的数据类型预测转炉中的终点碳含量和温度。模型中案例的属性不仅包括铁水成分、温度等单值类型的影响因素，还包括喷枪位置、氧气流量等时间序列类型的影响因素。检索过程中，基于欧氏距离和动态时间规整算法计算案例之间的单值数据相似度和时间序列数据相似度，然后加权得到综合相似度。然后基于生产数据研究了时间序列数据相似度的权重对预测精度的影响。最后，还将建立的模型的预测精度与基于 SVR 和 BPNN 的模型进行了比较。结果表明：随着时间序列数据相似性权重的增加，模型的预测精度先提高后降低。当时间序列数据相似度权重为0.4时，该模型的预测精度最高，优于SVR和BPNN模型。所建立的能满足现场生产的要求。最后，还将建立的模型的预测精度与基于 SVR 和 BPNN 的模型进行了比较。结果表明：随着时间序列数据相似性权重的增加，模型的预测精度先提高后降低。当时间序列数据相似度权重为0.4时，该模型的预测精度最高，优于SVR和BPNN模型。所建立的能满足现场生产的要求。最后，还将建立的模型的预测精度与基于 SVR 和 BPNN 的模型进行了比较。结果表明：随着时间序列数据相似性权重的增加，模型的预测精度先提高后降低。当时间序列数据相似度权重为0.4时，该模型的预测精度最高，优于SVR和BPNN模型。所建立的能满足现场生产的要求。4 并且优于 SVR 和 BPNN 模型。所建立的能满足现场生产的要求。4 并且优于 SVR 和 BPNN 模型。所建立的能满足现场生产的要求。