This study constructs and validates an AI-integrated “Chinese + Music” intelligent teaching model aimed at simultaneously enhancing international students’ Chinese language proficiency and musical professionalism. Through a 12-week controlled experiment (N=60), the findings reveal that, in terms of language learning, the intelligent speech feedback system improved students’ tone recognition accuracy by 27.2% (p<0.01) and grammar mastery by 37%. Notably, music-based tone training significantly reduced the confusion between the second and third tones, with error rates dropping by 72%. In the domain of professional instruction, the adaptive terminology system achieved an 88.7% mastery rate of musical terms, while spatiotemporal visualization technology increased the learning efficiency of music history by 40% and reduced study time by 43.8%. The AI composition platform further boosted the accuracy of pentatonic scale application by 61%. Qualitative analysis shows that intercultural creative tasks enabled 76% of students to deeply interpret the cultural connotations of musical symbols. The study also identifies boundary conditions in the application of AI: a 35% misjudgment rate in creative work evaluation, 20% of students requiring additional support for abstract theoretical instruction, and 15% exhibiting dependency on technology. In response, the paper proposes a “Dual-Channel Digital Humanities” development path, emphasizing the integration of a cultural annotation database, an adaptive difficulty matrix, and a human-machine collaborative mechanism to achieve a deep fusion of technological empowerment and humanistic education. This model provides an innovative paradigm for discipline-specific language teaching in the era of intelligent education.