AIGC-Driven Construction Path of Higher Vocational Arts and Crafts Professional Clusters via K-Means Clustering Analysis

Abstract

The rapidly emerging digitalization in the cultural and creative industry of craftsmanship and arts have highlighted the necessity of developing data science and AI teaching content of college and junior college education in talent cultivation. Due to that the cultural and creative industry of craftsmanship and arts had long been a supporting industry in the cultural and creative industry of Guangdong Province. The old methods of learning of arts and craftsmanship education and its manual skill transmission now face the crisis in innovating with AIGC technology.The purpose of the article is to build an improved line of professional cluster development for vocational arts and crafts on the basis of Artificial Intelligence Generated Content (AIGC) and machine learning. On the basis of K-Means method of group calculation, the article examines multidimensional statistics data of survey about people's age, education and industry and groups them naturally on the background of structure and cognition divergence of professional stakeholders. Through optimizing the within-cluster variance, the algorithm achieves a good classification of the different disciplines and the roles in the clusterable groups, facilitating the explanation of the role of tradition crafts and intelligent innovation and the changes to it. The results prove that such data analytics application will further improve the accuracy of curriculum matching, and achieve the integration across education, industrial and innovation systems.Through founding this quantitative logic of constructing professional clusters, this research has opened up a theoretical space and the practice path to modernization for vocational arts and crafts in China in terms of conserving cultural heritage of Chinese culture with digital transition.