Dynamic Recommendation System for Higher Vocational English Learning Paths Based on Real‐Time Knowledge Graph Update

ABSTRACT Aiming at the problem of insufficient individualization and dynamic demand in higher vocational English learning, this paper proposes a dynamic learning path recommendation method based on real‐time knowledge graph updates. By constructing a domain knowledge graph covering vocabulary, grammar, listening, speaking, reading, and writing skills in higher vocational English, and integrating multi‐source data such as learners' answering behavior and changes in mastery, an incremental graph real‐time update mechanism is designed to achieve dynamic adjustment of knowledge point relationships. Furthermore, based on the basic learning experience of higher vocational students, this system combines learner profiles with a graph neural network (GNN) model to generate personalized, explainable optimal learning paths. The system supports real‐time status tracking and personalized optimization. In a pilot study with 150 students, the system achieves an update latency of only 3.44 s under a load of 1000 times per hour, achieving an 86.7% recommendation accuracy rate, a 52.0% improvement in learning efficiency after 6 weeks, and a cumulative user satisfaction score of 75%. These results demonstrate that, compared to traditional static recommendation approaches, our system offers significant improvements in real‐time responsiveness, recommendation precision, and learning effectiveness, thereby providing a feasible technical solution with real‐time response capabilities for intelligent English learning in higher vocational colleges.