This study investigates the functional requirements of video conferencing platforms in Chinese higher education, where mobile learning has become increasingly essential. Based on data from 517 students and 211 teachers, we apply K-Means clustering and SHAP analysis to identify distinct user demand groups—low, medium, and high. Key features such as attention tracking, interactive tools, and feedback functions are found to significantly influence user preferences. Unlike traditional models focused on outcomes, this research emphasizes demand structure discovery through unsupervised learning. The findings offer actionable insights for platform developers and educational decision-makers, promoting the development of smarter and more personalized learning environments. This study also highlights the value of explainable AI in understanding user behavior and improving platform adaptability.
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