This study focuses on the construction and evaluation of a high-quality Chinese Manchu music dataset designed to facilitate Artificial Intelligence (AI) research and applications within cultural heritage and ethnomusicology. Through a systematic collection and organization of diverse Manchu music resources, including folk songs, dance music, and ceremonial pieces, this dataset effectively represents the cultural breadth of Manchu music. The dataset includes digitized and preprocessed audio data, with comprehensive metadata annotations, such as essential information, musical features, and cultural context, creating a robust foundation for AI-based analysis. Experimental evaluations highlight the dataset’s utility across various AI-driven applications: in music classification, using a CNN model, an accuracy of 90% was achieved in the “folk ensemble” category, with an overall accuracy of 85.7% and a precision of 82.3%. For music generation, a Generative Adversarial Network (GAN) model yielded a quality score of 7.8/10 and a Fréchet Audio Distance (FAD) of 0.32. In emotion recognition, the Random Forest model achieved 87% accuracy in identifying the emotion “joy”. These results underscore the dataset’s potential in supporting digital preservation and expanding AI applications in ethnic music classification, generation, and emotional analysis, contributing to both cultural heritage preservation and AI advancement in ethnomusicology.

With the rapid advancement of mobile Internet technology, the application of interactive mobile technology in education has emerged as a significant area of research, particularly in music education. Mobile technology has introduced transformative changes to both learning styles and teaching methods. Through the utilization of smart devices and mobile platforms, students are provided with personalized and flexible learning environments for music education, while teachers can leverage advanced technological tools to enhance teaching effectiveness. Although previous studies have examined the role of mobile technology in music education, most have focused on its isolated effects, neglecting a systematic analysis of the evolution of teaching effectiveness over time. Furthermore, existing methodologies often fail to account for temporal characteristics and dynamic changes, resulting in an incomplete evaluation of long-term educational outcomes. This study aims to analyze the network characteristics of interactive mobile technology applied in music education and to explore the evolution of teaching effectiveness with consideration of temporal characteristics. This study is expected to offer novel theoretical perspectives for the digital transformation of music education along with practical guidance for its implementation.