Background Internet rumors related to public health emergencies often trigger public panic and result in resource mismatches. During such crises, the public’s cognition is not fixed but continuously undergoes a dynamic process from initial construction to reconstruction, eventually solidifying into a stable “group cognitive space.” Investigating the structure of this space is crucial for understanding heterogeneous public behaviors and enhancing emergency governance. Method This study leveraged the LDA topic model and TF-IDF word frequency analysis, in conjunction with domain ontology, to construct a knowledge-dimensional thematic repository, thereby examining the evolutionary patterns of knowledge across distinct phases of public health emergencies. Additionally, the BTM algorithm and K-Means clustering were employed to excavate thematic elements of online rumors, while emotional orientations were dynamically monitored via Baidu AI’s sentiment analysis technology. Guided by Marzano’s classification theory, a cognitive questionnaire was developed for the classifica
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