Analysis of the Role of Decision Tree Algorithm in Art Education Based on the Background of the Internet of Things

Abstract

In recent years, transportation, medical care, energy, and other fields have relied on a number of smart technologies such as the Internet of Things and cloud computing, and the degree of intelligence has continued to increase. As a key part of the future development of cities, education is facing tremendous pressure and challenges. How to use all kinds of smart technologies to promote the informatization of education and create a new era of smarter education is an important mission of current smart education. Decision tree classification is a frequently used technique in data mining. It has the characteristics of fast classification and high efficiency. It is not only used to analyze data but also to make predictions. Professional art education occupies an important position in college education. How to combine decision tree algorithms to develop targeted and effective professional art education and to strengthen the teaching reform and research method innovation of art design has become the current teaching work of colleges and universities. The purpose of this paper is to optimize the teaching mode of art courses in colleges and universities, improve the teaching effect of art classes, and realize the teaching goal of art courses. In this paper, art curriculum teaching experience and the reality of teaching research, through analyzing three local questionnaire surveys of college students, based on the principle of students’ subjectivity from the topic content, organization form, evaluation mode of college art teaching in our student autonomy, initiative, and the role of creative mechanisms are discussed. According to the practical teaching activities of art courses conducted in this paper, the satisfaction degree of college students with art courses is up to 92%, which indicates that this experiment is still relatively successful and significant, and arouses students’ subjectivity to some extent.