Interactive Teaching Based on Artificial Intelligence and Its Application in Improving National Music Appreciation Ability

Abstract

The limitations of the traditional interactive teaching model are gradually becoming apparent in the current music teaching process. Based on the interactive teaching theory of artificial intelligence, this paper constructs an evaluation model of ethnic music appreciation ability and conducts research by using a novel method of fusion of long- and short-term audio features. The model proposes the classical features describing the sound quality and the beat histogram as the feature of the long-term rhythm of the music to form a mixed feature; secondly, the representation method of the song style vector is proposed, and the quantitative problem of music teaching is solved. During the simulation process, the model adopts the popular Model-View-Controller (MVC) design pattern and Unified Modeling Language (UML) and is developed with the Java 2 Platform Enterprise Edition (J2EE) architecture. It realized the user login function of three identities of students, teachers, and system administrators, and the subsystem can complete the management of students' personal information, browse related courseware information and other resource information, and download courseware and tutorials. The experimental results show that the average value of the students' evaluation is 0.606; the average value of case is 0.5852; it also reduces the workload of later maintenance of the system.