Article: Optimisation analysis of music and dance teaching mode based on intelligent communication technology and constitutional neural network Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.29 pp.29 - 50 Abstract: The rapid development of intelligent communication technologies has brought innovative opportunities to the field of music education, as traditional models are no longer sufficient to meet students' learning needs. This study employs a convolutional neural network (CNN) as the core algorithm to process audio and dance data, extracting high-level features such as rhythm and body movements through its multi-layered hierarchical structure. A support vector machine (SVM) algorithm is used to assess student abilities, while the convolutional neural network processes data to extract features, and intelligent sensor technology is integrated to build a teaching platform. The study found that the support vector machine achieved an accuracy of 93.7% in music feature classification, while the convolutional neural network improved the accuracy of dance movement classification to 96.3%. This model significantly improves the accuracy of teaching assessment, providing an intelligent solution for music and dance education and promoting human-computer interactive teaching. Inderscience Publishers - linking academia, business and industry through research

Title: Optimisation analysis of music and dance teaching mode based on intelligent communication technology and constitutional neural network

Authors: Huazhao Lu

Addresses: School of Arts, Taishan University, Taian, Shandong, 271000, China

Abstract: The rapid development of intelligent communication technologies has brought innovative opportunities to the field of music education, as traditional models are no longer sufficient to meet students' learning needs. This study employs a convolutional neural network (CNN) as the core algorithm to process audio and dance data, extracting high-level features such as rhythm and body movements through its multi-layered hierarchical structure. A support vector machine (SVM) algorithm is used to assess student abilities, while the convolutional neural network processes data to extract features, and intelligent sensor technology is integrated to build a teaching platform. The study found that the support vector machine achieved an accuracy of 93.7% in music feature classification, while the convolutional neural network improved the accuracy of dance movement classification to 96.3%. This model significantly improves the accuracy of teaching assessment, providing an intelligent solution for music and dance education and promoting human-computer interactive teaching.

Keywords: intelligent communication technology; constitutional neural network; CNN; music and dance teaching; intelligent sensor; artificial intelligence.

DOI: 10.1504/IJICT.2026.152577

International Journal of Information and Communication Technology, 2026 Vol.27 No.29, pp.29 - 50

Received: 29 Sep 2025
Accepted: 26 Dec 2025
Published online: 27 Mar 2026 *

Title: Optimisation analysis of music and dance teaching mode based on intelligent communication technology and constitutional neural network

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