Research on the Design and Performance Optimization of History Education Teaching System Based on Knowledge Graph and Sentiment Analysis

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.