Development Path of Tea Culture and Aesthetic Education Integration in Higher Vocational Colleges and Universities Based on Collaborative Filtering Algorithm

Abstract

Tea culture is an excellent traditional culture in China, and its integration into higher vocational aesthetic education can promote the overall development of students. This paper takes the higher vocational colleges and universities in H city as an example, constructs the correlation index of the integration and development of tea culture and aesthetic education, and explores the key factors of the integration and development of tea culture and aesthetic education by combining the gray correlation method. At the same time, the students’ portraits are constructed, the constructed user portraits are clustered using the K-mean algorithm, combined with the improved collaborative filtering algorithm, and the resources related to tea culture and aesthetic education in higher vocational colleges and universities are accurately recommended. The improved model is also applied to the city to analyze the resource recommendation effect of tea culture and aesthetic education and explore the development path of integration of tea culture and aesthetic education in higher vocational colleges. The results show that the gray correlation between the integration of tea culture and aesthetic education in higher vocational colleges and universities, and the educational foundation, educational level, student ability, and educational needs are within (0.642, 0.808). The similarity between the users recommending aesthetic education resources is above 0.7, the similarity between the users recommending tea culture and aesthetic education resources and the users recommending tea culture resources is above 0.8, and the content recommended by each user achieves the desired effect (100%). The effective promotion of tea culture and aesthetic education in higher vocational colleges and universities is achieved through the accurate recommendation of relevant resources for students’ needs.

Keywords

• gray correlation
• user profile
• k-mean
• collaborative filtering algorithm
• tea culture
• 05c85