This article’s goal is to discuss the effective passing mechanism of Chinese minority music under the environment of big data. Through the combination of Chinese nationality music components with contemporary nationality music market developing tendencies, it innovates the content of Chinese ethnic music inheritance to meet the personalized needs of modern listeners. This article uses an improved hierarchical clustering algorithm to analyze the attribute data of Chinese ethnic music. Firstly, through data cleaning and patching, a network of citation relationships between high-frequency words is constructed. Next, multiple similarity measurement methods such as Min’s distance, Jaccard distance, and Mahala Nobis distance were used to generate a similarity matrix for mixed Chinese ethnic music attribute data, and a hierarchical clustering algorithm based on symmetric regularization was used for clustering solution. Research has found that researchers highly focus on themes such as ethnic music culture education, cultural inheritance and development, and often regard ethnic music teaching as an important way to achieve the inheritance and development of ethnic music culture. Meanwhile, through the analysis of high-frequency keywords and co-occurrence matrices in Chinese ethnic music, the research hotspots and relationships between keywords in the field of Chinese ethnic music were revealed. In the context of big data, the integration of main stream ethnic music components, the exploration of special ethnic music styles and expression methods, and the use of data analysis tools to comprehend audience liking are the keys to push the effective handing down of Chinese ethnic music.The improved hierarchical clustering algorithm provides strong data support for the creation of Chinese ethnic music inheritance content, which helps to create more attractive and unique Chinese ethnic music works, further promoting the inheritance and development of Chinese ethnic music.
