Artificial Intelligence-Driven Personalized College English Vocabulary Learning Path Generation and Optimization

In recent years, the construction of education informatization has been comprehensively promoted, and the personalized learning recommendation model has brought a new direction for the development of intelligent learning platform for college English vocabulary.This study constructs the KCPE-SR model based on collaborative filtering algorithm and knowledge graph, generates and optimizes the suitable personalized learning paths for learners through the interaction between learners of college English vocabulary and resources, and develops a personalized college English vocabulary learning system based on this model.The analysis of the application effect of the system reveals that the experimental class students' English vocabulary learning performance has been significantly improved with the help of the personalized learning system, and the students' English vocabulary knowledge mastery (20.00 points) and vocabulary comprehensive application ability (20.49points) have also increased.The personalized college English vocabulary learning path generation and optimization system proposed in this paper is able to achieve accurate personalized recommendation of learning resources and can meet the needs of college English vocabulary learning.