The rapid development of information technology provides new opportunities for educational reform, and the application of big data technology in the field of education is becoming more and more widespread. Aiming at the problems of insufficient precision and inefficient data processing in the monitoring of the effect of ideological education, this study constructs a model for monitoring the effect of ideological education in a big data environment based on high-performance computing support. Methodologically, the improved particle swarm algorithm is used to optimize the K-means clustering algorithm, the inertia weight is improved by introducing the Sigmoid function, the position update formula is optimized by combining the time weight, and the weighted Euclidean distance is applied to improve the clustering accuracy. Then a complete monitoring system containing data collection, preprocessing and analysis is constructed, and MapReduce framework is applied to realize high-performance big data computing. The results show that using 14,620 student civic education data from a university in 2024 for validation, the improved algorithm converges to a steady state after 20 iterations, the distribution of students’ grades is normally distributed within the interval of 70 to 100 points, and the best effect is achieved when the number of clustering categories is set to 14. Through 3-dimensional feature analysis, the students were successfully classified into three categories, namely, high scoring segment, middle segment and low scoring segment. The study shows that the model can effectively improve the accuracy and efficiency of monitoring the effect of civic education, providing scientific basis and technical support for realizing personalized civic education.