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Framework for Building Knowledge Map of Ethnic Music Based on Big Data

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DOI: 10.23977/jeis.2024.090111 | Downloads: 3 | Views: 125

Author(s)

Jinglin Peng 1, Jianfeng Zhou 2

Affiliation(s)

1 College of Humanities and Arts, Nanyang Agricultural Vocational College, Nanyang, Henan, China
2 Nanyang Lida Optic-electronics Co., Ltd., Nanyang, Henan, China

Corresponding Author

Jinglin Peng

ABSTRACT

National music is a cultural treasure with unique charm and charm in the traditional Chinese culture, which has high research value and broad social influence. This paper aimed to explore the construction of ethnic music knowledge map based on big data analysis. This paper proposed the knowledge map of ethnic music and big data clustering analysis, and studied the experimental results of constructing the knowledge map of ethnic music based on this research. The experimental results of this paper showed that the knowledge map provided a scientific framework and method for the exploration of ethnic music knowledge. It made a reasonable explanation and evaluation for researchers in the field of ethnic music and pointed out the direction for the development of ethnic music. This paper identified 24 high-frequency keywords, which are the basis of co-word analysis. Among them, "national music" appeared 1940 times, and "ethnomusicology" appeared 465 times. "Folk music" appeared 415 times, and "music tradition" appeared 276 times. "Chinese music" appeared 270 times. Multivariate statistical methods are often used in co word analysis. These are the central links in co word analysis. Clustering analysis was used to classify keywords in ethnic music, thus revealing the current hot topics in ethnic music. In a word, the construction framework of ethnic music knowledge map based on big data analysis is conducive to the development of ethnic music.

KEYWORDS

Knowledge Map, Big Data Analysis, National Music, Co-word Analysis

CITE THIS PAPER

Jinglin Peng, Jianfeng Zhou, Framework for Building Knowledge Map of Ethnic Music Based on Big Data. Journal of Electronics and Information Science (2024) Vol. 9: 69-79. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2024.090111.

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