Precision Civic Education is a new trend in Civic Education, the realization of which needs to rely on the mining of Civic Education big data and information management. K-means data mining algorithm is simple and efficient, but easy to fall into the local optimum, so this paper introduces the Canopy clustering algorithm to determine the optimal number of clusters quickly, and replaces the initial clustering of the random selection method by the maximum and minimum distance algorithm to improve the efficiency of clustering while avoiding the K-means algorithm falling into the local optimum. means algorithm to avoid falling into local optimization while improving the clustering efficiency. On the basis of the improved algorithm, the informationization platform of Civic Education Management is constructed through demand analysis and architecture design. The optimized clustering algorithm accurately realizes the clustering of students’ consumption and study information, and supports the construction of the informatization platform. After using the platform of this paper, the performance of students’ civic education dimensions such as learning motivation, learning energy, concentration, cultural literacy, information cognition, etc. is significantly improved, which verifies the enhancement of the effectiveness of students’ civic education by constructing the education management informatization platform.