Aiming at the problems of lagging information feedback, imperfect early warning and intervention mechanisms, and weak risk control ability that exist in the current work of ideological education in colleges and universities, this paper establishes a combination of a simple Bayesian algorithm of ideological dynamic early warning mechanism. This study takes school A as an example, collects a large volume of log data features based on user behavior, and after preprocessing, fuses the decision tree algorithm and the plain Bayesian algorithm and integrates them into the early warning classifier to establish a dynamic early warning model for civic education, and carries out experimental simulation and practical application to test its performance and early warning results. The results show that the prediction error indexes of this paper’s algorithm are no more than 0.1, and the abnormal rate of Civics behavior in each dimension is basically within 10%, which shows high adaptability. This study is beneficial for universities to carry out student civic education work and help civic workers better guide students and serve them.