Cultivating innovative talents is an important goal of the talent cultivation strategy in the new era, and ideological and political education is an indispensable part of cultivating a correct outlook on life and the spirit of innovation. In order to fully leverage the supporting role of ideological and political education in cultivating innovative talents, this paper proposes an enhanced collaborative filtering algorithm and content recommendation algorithm. The traditional Civic and Political learning evaluation algorithm is improved by considering the forgetting factor and introducing disciplinary abilities and sequence dependence. The results of the experimental analysis point out that the average grade improvement of the seven Civics and Politics knowledge points of the students in the experimental class reaches 10.3%, the mean value of the four innovation dimensions reaches 14.95 points, and the average agreement rate between the self-assessment results of the learning effect and the results of the evaluation model is 90%. It shows that the optimized Civics personalized learning and evaluation method, based on this paper, has positive effects.