Traditional Chinese language teaching methods are difficult to meet students’ individualized needs, while fuzzy logic inference technology can accurately assess learners’ characteristics, provide targeted learning resources, and effectively improve learning outcomes. In this study, fuzzy logic inference technology is used to establish a fuzzy control rule system to predict learners’ performance by taking four dimensions, namely, average daily online learning time, average daily vocabulary growth, average answering time, and total vocabulary mastered, as input variables, and then matching Chinese vocabulary learning resources of corresponding difficulty. The experiment selects 60 Chinese majors in a university as the research subjects, randomly divided into the experimental group and the control group of 30 people each, and verified through a 9-month teaching experiment. The results showed that the experimental group using the personalized learning path based on fuzzy logic reasoning was significantly better than the control group with traditional teaching methods after the teaching intervention (P=0.003<0.05). The within-group comparison analysis showed that the students' Chinese test scores before and after the intervention in the experimental group exhibited significant differences, while the control group did not produce significant changes. Through the application of fuzzy set construction and fuzzy relationship matrix, this study successfully applies fuzzy logic reasoning technology to the field of Chinese language education, which confirms the effectiveness of this method in enhancing learning effects and provides new technical support and practical reference for personalized teaching in Chinese language education.
