As a representative of the new generation of information technology, reinforcement learning technology has received more and more attention, but no scholars have yet applied reinforcement learning recommendation technology to ideological and political education. In this paper, a diversity optimization recommendation algorithm for ideological and political education is constructed, and the vector representation model of the state is designed and optimized based on the Markov decision process. Through the actor-critic algorithm, to calculate the probability of recommendation sampling to generate a recommendation list and then establish a decision model in order to improve the ideological and political education recommendation algorithm on the user’s feedback of accurate judgement and recommendation decision-making effect. The experimental results show that the Diversity Opt performs well in terms of prediction error. The algorithm is ahead of the comparison algorithm by an average of 9.35% and 8.3%, respectively, and all other performances are also significantly better than the comparison algorithm. The average score of students’ performance in the experimental class applying the algorithm is 14.4 points higher than that of the control class. It shows that the recommendation algorithm helps to accurately recommend ideological and political education and exhibits beneficial practical effects.