With the continuous development of technology, personalized learning has gradually become an important trend in the field of education. This study proposes a Bayesian network-based personalized learning path planning method for art education. First, the personalized needs of learners are analyzed, and a Bayesian network model is used to determine learners’ mastery of art knowledge points and recommend adaptive learning resources. Then, the study uses the Felder-Silverman learning style model and Bayesian classifier, combined with the learner’s online behavior, to calculate the similarity between the learner and similar learners, so as to recommend relevant learning resources for the target learner. The experimental results show that the personalized recommendation system significantly improves the students’ art scores, and the scores of the students in the experimental group increase from 6.94 to 8.93 in the pre-test, which is an improvement of 1.99 points. Through the personalized recommendation of learning resources, learners’ learning efficiency is significantly improved and their learning attitude is more positive. The study shows that the personalized learning path recommendation system based on Bayesian network can effectively improve the learning effect of students and promote the mastery of their art knowledge and ability.
