Research on Optimizing the Dissemination and Development Pathways of Chinese Music Internationally Based on Large-Scale Data Mining
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Abstract
Against the backdrop of cultural globalization and the rapid development of digital communication technologies, the international dissemination of Chinese music has become an important pathway for enhancing cultural influence and promoting cross-cultural communication. To quantitatively evaluate current dissemination effectiveness and identify optimization opportunities, this study proposes a large-scale data mining framework based on four dimensions: dissemination entities, content carriers, channel matrices, and audience feedback. Dissemination data collected from major international digital platforms, including Spotify, YouTube, and TikTok, during 2019–2024 are integrated with academic literature datasets from Web of Science and Scopus. Scrapy-based data acquisition, Louvain community detection, social network analysis, and a BERT-enhanced sentiment computing model are employed to investigate dissemination scale, geographical distribution, audience characteristics, content preferences, and communication barriers. The results indicate that Chinese music has achieved sustained growth on international platforms, while significant regional imbalance and limited penetration in mainstream Western markets remain. Fusion music demonstrates the strongest dissemination performance, whereas audience engagement is highly associated with cultural exploration and cross-cultural adaptation. Based on the analytical results, optimization strategies involving intelligent communication networks, data-driven audience profiling, content adaptation, and multi-channel collaborative dissemination are proposed. The study provides methodological support for global cultural communication and offers references for information propagation analysis, communication-network optimization, and intelligent signal-driven dissemination systems.
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