Research on the Design and Performance Optimization of History Education Teaching System Based on Knowledge Graph and Sentiment Analysis
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Abstract
The increasing integration of intelligent educational platforms with distributed computing and communication infrastructures has created new requirements for efficient information transmission and adaptive resource management. This study proposes a history education teaching system based on knowledge graphs and sentiment analysis, aiming to improve learning engagement, cognitive assessment, and instructional efficiency. A distributed multimodal knowledge graph is developed to integrate textual, visual, and behavioral information through cross-modal semantic alignment, while a lightweight sentiment analysis framework enables low-latency emotion recognition and dynamic teaching feedback. To further enhance system performance, an edge–cloud collaborative architecture, joint emotion–cognition reasoning mechanism, and optimized knowledge update strategy are incorporated to support high-concurrency educational scenarios. Experimental evaluations demonstrate significant improvements in student participation, knowledge mastery, resource matching accuracy, and cultural knowledge retention, together with reduced response latency and enhanced computational efficiency. Beyond intelligent education, the proposed framework provides valuable insights for large-scale digital learning environments supported by wireless communication networks and distributed information transmission systems, where reliable data delivery and efficient resource scheduling are essential for real-time interactive services.
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References
J. Feng, “Improving sentiment analysis in literary texts through bidirectional encoder representations: A BERT-based approach,” Journal of Computational Methods in Sciences and Engineering, vol. 25, no. 5, pp. 4650-4664, 2025, doi: 10.1177/14727978251338004.
Q. Zheng, T. Guo, G. Ding, H. Zhang, and K. Zhang, “Manufacturing service recommendation method based on knowledge graph and graph convolutional network,” Advanced Engineering Informatics, vol. 68, no. PA, pp. 103599-103599, 2025, doi: 10.1016/j.aei.2025.103599.
S. Yang, F. Cao, K. Yu, and J. Liang, “Chain graphs structure learning given local background knowledge,” International Journal of Approximate Reasoning, vol. 186, pp. 109524-109524, 2025, doi: 10.1016/j.ijar.2025.109524.
Y. Huang, R. Wang, Y. Li, G. Wang, and Y. Yan, “Knowledge graph-driven methodology for complex product archi-tecture solution generation and simulation verification,” Advanced Engineering Informatics, vol. 68, no. PA, pp. 103590-103590, 2025, doi: 10.1016/j.aei.2025.103590.
J. Wang, Y. Chen, and Y. Li, “Evaluation and Feedback System for Physical Education Teaching Effec-tiveness Based on Artificial Intelligence,” International Journal of Computational Intelligence Systems, vol. 18, no. 1, pp. 303-303, 2025, doi: 10.1007/s44196-025-01046-5.
Y. Han, H. Zhang, and Y. Man, “A method for constructing fault knowledge graphs based on an improved hidden Markov Model: A case study for papermaking industry,” Journal of Cleaner Production, vol. 520, pp. 146079-146079, 2025, doi: 10.1016/j.jclepro.2025.146079.
N. Zhou, L. Sun, M. Han, and S. He, “PSA-GAT: Integrating position-syntax and cross-aspect graph atten-tion networks for aspect-based sentiment analysis,” Data & Knowledge Engineering, vol. 160, pp. 102477-102477, 2025, doi: 10.1016/j.datak.2025.102477.
Q. Liu, S. Feng, and M. Huang, “TEQA: Temporal knowledge graph enhanced question answering,” Knowledge-Based Systems, vol. 325, pp. 113916-113916, 2025, doi: 10.1016/j.knosys.2025.113916.
S. M. Hossain, “Emotional drivers of sustainable AI adoption: A sentiment analysis of early user feed-back on the deepSeek app,” Sustainable Futures, vol. 10, pp. 100947-100947, 2025, doi: 10.1016/j.sftr.2025.100947.
M. Ramasamy and M. Elangovan, “Development of optimized cascaded LSTM with Seq2seqNet and transformer net for aspect-based sentiment analysis framework,” Web Intelligence, vol. 23, no. 3, pp. 295-318, 2025, doi: 10.3233/WEB-230096.
X. Wang, Y. Cheng, P. Cheng, and X. Zhang, “AttDNet: An attribute-driven deep learning network for visual emotion analysis,” Pattern Recognition, vol. 171, no. PA, pp. 112098-112098, 2026, doi: 10.1016/j.patcog.2025.112098.
J. You, H. Li, J. Deng, W. Li, Y. He, and F. Ren, “Hierarchical Reasoning Enhanced Few-Shot Multimodal Sentiment Analysis,” Neurocomputing, vol. 651, pp. 130883-130883, 2025, doi: 10.1016/j.neucom.2025.130883.
F. Fischer, “Teaching Trump:Critical Issues in History and Civics Education,” Taylor & Francis, vol. 04, no. 08, pp. 16-18, 2025, doi: 10.4324/9781003494195.
X. Guan, “Design and Application of a Teaching System Based on Mobile Internet,” International Journal of High Speed Electronics and Systems, vol. (prepublish), pp. 18-20, 2025, doi: 10.1142/S012915642540498X.
B. Du, “The Introduction of Popular Music in Music Education Teaching in Vocational Colleges,” Journal of Education and Culture Studies, vol. 8, no. 4, pp. 18-20, 2024, doi: 10.22158/jecs.v8n4p183.
C. Hajisoteriou, E. A. Solomou, and M. E. Antoniou, “Teaching history for global citizenship through an in-tercultural perspective,” Frontiers in Education, vol. 9, pp. 1435402-1435402, 2024, doi: 10.3389/feduc.2024.1435402.
G. O’Brien, “History repeating itself: an investigation of the challenges of teaching civic, social and political education,” Irish Educational Studies, vol. 43, no. 4, pp. 1021-1042, 2024, doi: 10.1080/03323315.2023.2189134.
X. Wu, “Exploration of Teaching Reform of Chinese Urban Construction History Course Based on Moral Education,” Higher Education and Practice, vol. 1, no. 10, pp. 18-22, 2024, doi: 10.62381/H241A01.
R. Göransdotter, “Looking Ahead: Nordic and British Perspectives on History of Education,” History of Education Quarterly, vol. 64, no. 3, pp. 347-351, 2024, doi: 10.1017/heq.2024.27.
A. K. Juyani, F. Zarei, and R. Maasoumi, “Efficacy of mobile-based educational intervention using Instruc-tional Systems Design in promoting preventive behaviors for sexually transmitted infections among Iranian women: A randomized controlled trial,” BMC Public Health, vol. 24, no. 1, pp. 510-510, 2024, doi: 10.1186/s12889-024-18002-1.
K. Zhang, “Design and application of intelligent teaching system for network and new media major driven by artificial intelligence technology,” International Journal of Embedded Systems, vol. 17, no. 1-2, pp. 150-159, 2024, doi: 10.1504/IJES.2024.143759.
A. Kruja, V. Ndrecaj, A. Çela, F. Morina, and E. Hysa, “Towards an Education for a Circular Economy: Mapping Teach-ing Practices in a Transitional Higher Education System,” Sustainability, vol. 17, no. 21, pp. 9787-9787, 2025, doi: 10.3390/su17219787.
M. K. Jones, “Centering Teaching and Learning Centers in Instructional Systems Design Conversations,” TechTrends, vol. 66, no. 6, pp. 903-904, 2022, doi: 10.1007/s11528-022-00786-x.
W. Chen, “Design of Music Teaching System Based on Artificial Intelligence,” Mathematical Prob-lems in Engineering, vol. (1), Art. no. 2627395, 2022, doi: 10.1155/2022/2627395.
G. Chen, “Design of College English Halving Classroom Teaching System Based on Big Data,” Journal of Physics: Conference Series, vol. 2066, no. 1, pp. 18-19, 2021, doi: 10.1088/1742-6596/2066/1/012082.
C. Duan, “Design of online volleyball remote teaching system based on AR technology,” Alex-andria Engineering Journal, vol. 60, no. 5, pp. 4299-4306, 2021, doi: 10.1016/j.aej.2021.03.006.
H. Dong, “Adapting during the pandemic: A case study of using the rapid prototyping instructional system design model to create online instructional content,” The Journal of Academic Librarianship, vol. 47, no. 3, pp. 19-20, 2021, doi: 10.1016/j.acalib.2021.102356.