Under the background of globalization and knowledge economy, the importance of innovation and entrepreneurship education for college students is becoming more and more prominent. This paper combines fuzzy logic and decision tree algorithm to construct a cultural confidence recognition model of innovation and entrepreneurship education. Feature selection and classification are carried out on the salient features of the collected data information on innovation and entrepreneurship education. First, eight types of statistical features, such as the degree of integration of excellent traditional culture, the degree of value leadership and moral cultivation, the innovative power of grounded cultural knowledge, and the effect of social responsibility cultivation, are extracted as inputs to the C4.5 algorithm, and a decision tree is constructed for feature selection. Then, according to the constructed decision tree, the affiliation function and IF-THEN rule of the fuzzy inference model are designed. Finally, the designed fuzzy inference model is used to classify the degree of cultural confidence. The method achieves 100% accuracy in recognizing the lack of cultural self-confidence in innovation and entrepreneurship education, and more than 90% in recognizing the overall effect of general cultural self-confidence and rich cultural self-confidence. The experimental results show that the combination of decision tree and fuzzy inference modeling is feasible for the detection and classification of college students’ innovation and entrepreneurship education, and has strong practical application value.