International Journal of Information Technology and Applications, Vol. 2, No. 4, pp. 1-17, December 2025.
Abstract: As an important local opera genre in Fujian Province, Min Opera carries profound historical and cultural value but is also facing practical challenges such as a shrinking audience base and insufficient communication power. In the context of the rapid development of digital humanities, exploring digital protection and regeneration pathways for Min Opera is of great significance. Based on empirical data collected through questionnaires, this study employs Python descriptive statistical analysis and Weka data mining methods to systematically examine audience age structure, media preferences, and interest levels. The findings indicate that age is the core variable influencing audience interest: the younger generation shows significant differentiation in interest, the middle-aged group demonstrates relatively stable overall interest, while the elderly generally maintain a strong sense of cultural identity. Moreover, media preferences and educational background play important moderating roles across different groups. Furthermore, this paper proposes digital regeneration strategies for Min Opera, including database construction, immersive experience design, user interaction and co-creation mechanisms, as well as cultural and creative industry expansion. The innovation of this study lies in combining digital humanities theories with data mining methods, which not only reveals the structural differences of Min Opera audiences but also provides practical references for the digital preservation and dissemination of traditional opera. Based on 300 valid questionnaire samples, the decision tree (J48) achieved an overall prediction accuracy of 100% on the test set; the K-means clustering (k=3) further identified three audience groups: “Traditional Loyalists,” “Potential Developers,” and “Low Participation,” with clustering centers shown in Figure 5.