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University-level aesthetic education can help students develop their aesthetic sensibilities, enhance their artistic and creative skills, maximise their artistic and creative potential, and produce more and higher-quality works of art. Aesthetic ability is crucial for students’ quality because it helps them better appreciate the appeal of artistic creations, enhance their art education, and advance in their future growth and development. Therefore, it should place a high value on aesthetic education at the university and work at various levels to gradually cultivate students’ sentiments and enhance their aesthetic skills. A new “Big Data era” has emerged as a result of the expanding and widespread use of IT and the massive amount of data that needs to be stored and used. There are several widely used methods for classifying data, including decision trees, Bayesian classification, Bayesian networks, and neural networks. In order to more effectively develop students’ aesthetic ability in college aesthetic education teaching activities, the impact of college aesthetic education on college students’ creative ability and artistic literacy was examined. This was done on the basis of a decision tree classification model. According to the experimental results, the decision tree model significantly increased the accuracy of preference classes while maintaining the same level of overall accuracy. The F-value of the decision tree model on various data sets was improved by 0.318 and 0.221, respectively, in comparison to the SVM algorithm. The development of innovation ability in university aesthetic education is crucial for developing students’ personalities, bettering study habits, enhancing aesthetic literacy, and developing comprehensive ability. As a result, we analyze the impact of university aesthetic education on college students’ innovation ability and artistic literacy using the decision tree classification model.
Minghui Wang (Tue,) studied this question.