[Purpose/significance] Government rumor refutation is widely recognized as an effective means to curb the spread of rumors, with social media users showing variability in their interaction with refutation information. This study,by analyzing the characteristics of individual users' interaction behaviors with government refutation information, iden⁃tifies the key factors and their impact on the information interaction effectiveness among user groups. It aims to further refine theories related to information interaction and provide theoretical guidance for the scientific refutation efforts of government departments. [Method/process] Based on the traditional cellular automata model and drawing on the SEIR model theory, this approach subdivides user groups according to the specific information states of individual us⁃ers. It quantitatively describes users' interaction states with refutation information and utilizes MATLAB R2022a for simulation. This process constructs a model of government rumor refutation information interaction behavior that incor⁃porates individual user differences. Furthermore, it analyzes important parameters of the model to identify key factors.[Result/conclusion] The effectiveness of social media users' interaction behaviors with government rumor refutation in⁃formation is primarily influenced by different government refutation interaction strategies, the removal rate of govern⁃ment refutation information, and the distribution patterns of refutation participants. Among these, when considering dif⁃ferent government refutation interaction strategies, comprehensive intervention strategies prove to be more effective than adjusting social distances and following opinion leaders. The lower the removal rate of government refutation infor⁃mation, the better the interaction effect of government refutation information. The distribution patterns of refutation par⁃ticipants can be broadly categorized into random, concentrated, and uniform types, with the random distribution of refu⁃tation participants yielding the best interaction effect with government refutation information.