Path analysis of students’ aesthetic education and individualized construction of career guidance based on fuzzy logic

This research focuses on path analysis of students’ aesthetic education and individualized construction of career guidance based on fuzzy logic. Unlike conventional fuzzy recommendation systems that merely apply fixed rule bases, this study explicitly addresses three limitations of existing approaches: (a) static rule bases that do not incorporate periodic feedback from student outcomes; (b) absence of domain-specific rule design grounded in aesthetic education theory; and (c) lack of interpretable linkage between aesthetic education inputs and career guidance outputs. The proposed system integrates a fuzzy inference engine with a dynamic feedback loop that adjusts membership function parameters based on periodic student re-evaluations. Experimental results show that the model achieves 83.2% top-1 accuracy, which is comparable to existing fuzzy recommenders, but with the added advantage of providing rule-level explanations for each recommendation. The contribution lies not in claiming novelty of fuzzy logic itself, but in tailoring the rule structure and feedback mechanism specifically to the aesthetic education domain, which has been underexplored in prior fuzzy recommendation literature.