A new personalized resource recommendation method is proposed for laboratory-integrated Civics teaching, utilizing multi-source heterogeneous information fusion. The process involves collecting and pre-processing data on Civics teaching resources in the laboratory, constructing an information fusion model to integrate information from various sources effectively, analyzing user characteristics based on data mining results, and designing a personalized resource recommendation scheme. Test results demonstrate that the proposed method significantly improves precision and recall rates of resource recommendation. This method ensures a strong alignment with Civic and Political Education resources, fosters students’ interest in Civics and Political Education courses, and achieves excellent recommendation outcomes.