This study focuses on the extraction of ethnic music cultural genes and green communication pathways from a digital humanities perspective. Addressing the digital transformation challenges faced by traditional ethnic music preservation, the study constructed a multimodal cultural gene extraction framework integrating deep learning, natural language processing, and computer vision, extracting 687-dimensional cultural features from 12 types of ethnic music, with the model achieving an F1 score of 0.863. A green communication system based on a cloud-edge-device collaborative architecture with 291 nodes was designed, achieving an energy efficiency ratio of 36,300 people per kilowatt, representing a 62.8% improvement over traditional architectures and an annual carbon emission reduction of 1076.8 t. A real-time energy consumption monitoring and carbon emission accounting system covering six major scenarios was established, with mobile-end optimization rates reaching 52.3%, translating green communication into quantifiable indicators. Systematic solutions were proposed for technical challenges such as sample imbalance and high-dimensional sparsity, increasing data availability to 91.2% and system availability to 99.7%. The successful implementation of 12 projects validated the feasibility of translating theory into practice. The research outcomes provide a computable methodological paradigm for the digital preservation of ethnic music, with potential for extension to digital museums, online education, and other fields, contributing a Chinese solution to the green transformation of the global digital cultural industry.