Design and Implementation of a Knowledge Graph-Based Teaching Assistance System for English Courses in Colleges and Universities

In order to enhance the personalization and intelligence of English teaching in colleges and universities, a teaching assistance system based on knowledge graph is designed and implemented. The system integrates multi-source data through a four-layer distributed architecture and adopts a knowledge processing method combining deep learning and rule extraction to construct a perfect ontology model and knowledge graph. The system provides personalized learning paths and ability assessment through intelligent recommendation, visual navigation and automatic assessment functions. Experimental results show that the system has significant advantages in enhancing students' learning effectiveness and ability development, especially in course resource recommendation and learning behavior analysis.