Innovative Cultural Transmission Paths Analysis for Local Opera Based on Data Mining

With the rapid development of society and cultural diversity, local opera, as a representative of traditional art, faces dual challenges of inheritance and innovation. The inheritance of traditional Chinese opera often relies on oral instruction, but in modern society, this method is no longer suitable for the rapidly changing cultural environment. The purpose of this study is to explore in depth the innovative cultural dissemination path of local opera through data mining techniques. This paper uses data mining techniques to explore and analyze the inheritance of innovative culture in local opera, and to study the inheritance paths. This paper proposes a data mining-based method for analyzing and identifying the paths of inheritance of innovative culture in local opera. This paper focuses on two aspects of technology clustering and technology theme association, and the research paths are divided into flexible clustering based on data enhancement and technology evolution path identification based on theme extraction. Firstly, the heritage changes of dating tunes are discussed. Secondly, the unique artistic esthetics of the dating tune is compared with the artistic characteristics of Henan opera and the three-stringed book. Through the research and learning experience of the Dai Yongqu, it is easier to thoroughly understand the artistic essence and cultural connotation of the Dai Yongqu, and that exposure to the Dai Yongqu is a process of touching traditional culture. In the text clustering section, the data co-occurrence frequency matrix is constructed based on the results of multiple clustering, transforming the problem of determining the number of clusters into a co-occurrence frequency problem with semantic information of the text, thus achieving the purpose of flexible clustering on demand. In a comparison with existing methods, the text representation method using data-enhanced coding achieves better semantic coding results. The flexible clustering method can differentiate the data better than the clustering method with a defined number of clusters and obtains clustering results with high intra-class similarity and high inter-class differentiation.