Exploration of Dance Teaching in Colleges and Universities under the Background of Artificial Intelligence and Artistic Quality Education of College Students

Cultivating composite talents to meet the needs of the rapidly developing society has become an important task for colleges and universities nowadays, so the cultivation of students’ artistic quality is particularly important. Based on the visual framework of the art quality education data of college dance teaching and college students’ artistic quality, this paper USES the data mining technology to store the education data in the data warehouse and to visualize the processing by on-line analysis. After completing the data mining, the K-means clustering algorithm based on SOM clusters the mined educational data, combines the correlation analysis method and the association rule method to mine the interrelationship between the two, and explores the development path of the two. The study shows that the performance of college dance teaching and college students’ artistic quality education is divided into three categories: excellent (56.6%), average (31.8%) and poor (11.6%), and the support degree of each correlation rule between college dance teaching and college students’ artistic quality education is obtained to be greater than 0.15, and the confidence level is greater than 0.6, and college dance teaching and college students’ artistic quality education are significantly related. This paper provides a new research direction for the development of college dance teaching and college students’ artistic quality education.