[Purpose/significance] The sustainable development of knowledge payment platforms relies heavily on the active participation of both knowledge producers and knowledge consumers. Accurately identifying these two types of key users is crucial for optimizing platform operations and fostering a healthy content ecosystem. [Method/process]Based on the context of knowledge payment platforms, this study proposes a key user identification framework that integrates multi-dimensional features. The framework characterizes user behavior from three dimensions: activity, professionalism, and commercial value. It incorporates BERT-based sentiment analysis to quantify the emotional tendencies in user interactions. Furthermore, a sentiment-weighted LeaderRank algorithm is employed to calculate sentiment scores, and the entropy weight method is used to determine indicator weights. Finally, a comprehensive key user identification system is constructed by integrating these multi-dimensional features, generating a final ranking of user criticality. [Result/conclusion] Empirical research on the Ximalaya FM platform demonstrates that the proposed method effectively identifies top knowledge producers with high commercial value, while also capturing vertical domain experts and sentiment-driven long-tail users. The identification results are highly consistent with the platform′s ecological structure. Key knowledge consumers can be classified into three categories: core-value type, complementary-advantage type, and long-tail potential type, reflecting differentiated behavioral patterns under multi-dimensional features.This provides an empirical basis and methodological support for platforms to implement precise hierarchical user management.