Hybrid Intelligent Teaching System for Classical Texts Towards Deep Learning: Construction and Application Based on Deep Semantic Understanding and Knowledge Graphs

Amid the in-depth integration of artificial intelligence technology into the field of education, natural language processing and knowledge graph technologies have provided new possibilities for the digital transformation of classical text teaching. Faced with the widespread dilemma of in-depth interpretation in classical text teaching, this study proposes a hybrid intelligent auxiliary system featuring the collaboration of "human teacher-AI agent-learner". By integrating deep semantic understanding models with dynamic knowledge graphs, the system constructs a three-layer architecture supporting dynamic classroom inquiry, and realizes key technologies such as domain-adaptive pre-training for literature, semi-automated knowledge graph construction, and teaching-oriented interactive recommendation. A six-week quasi-experiment shows that the system can effectively improve students' depth of interpretation and ability of meaning connection, while enhancing teachers' teaching guidance efficiency. This research provides an operable and verifiable hybrid intelligent practice path for the cultivation of higher-order humanistic literacy in the era of artificial intelligence.