计算机应用研究2026，Vol.43Issue(3)：681-688,8.DOI:10.19734/j.issn.1001-3695.2025.07.0257
知识图谱增强的残差集成网络舆情热度预测方法
Knowledge graph-enhanced residual ensemble method for online public opinion popularity prediction
摘要
Abstract
To address the challenge of integrating multi-source heterogeneous data and capturing deep semantic correlations in public opinion prediction,this paper proposed a knowledge graph-enhanced auto-residual regression ensemble(KG-ARRE)framework.The KG-ARRE framework extracted daily features from microblog data,built a subject-object interaction know-ledge graph using textual semantic analysis,and applied an auto-regressive integrated moving average(ARIMA)model to ob-tain residuals from the popularity time series.Then it combined the multi-source features by temporal convolutional network(TCN)for modeling.The framework also used multiple randomly initialized sub-models and an error-weighted ensemble me-chanism to improve prediction stability.Experiments on real-event datasets show that KG-ARRE achieves a mean absolute per-centage error(MAPE)of 4.53％,which is 63.1％lower than that of the baseline TCN(MAPE=12.28％).When the knowledge graph and ensemble modules are transferred to models such as BiLSTM,the MAPE decreases by 2.36～7.05 per-centage points.These results demonstrate that KG-ARRE enhances prediction accuracy and generalization.关键词
网络舆情/TCN/残差集成/知识图谱/时序建模Key words
online public opinion/TCN/residual ensemble/knowledge graph/temporal modeling分类
信息技术与安全科学引用本文复制引用
杨敏,李明伍,彭国莉,吴方龙..知识图谱增强的残差集成网络舆情热度预测方法[J].计算机应用研究,2026,43(3):681-688,8.基金项目
四川省哲学社会科学基金资助项目(SCJJ23ND12) （SCJJ23ND12）
四川省社会科学重点研究基地资助项目(SCAA25-B12,SCAA24-B17) （SCAA25-B12,SCAA24-B17）
