Standardized individualized recommendation techniques for ideological and political content predominantly use the community engagement score (CES) algorithm to judge the user's characteristic behavior, which is vulnerable to changes in the weight of the recommendation momentum factor, resulting in poor recommendation performance. Therefore, a new personalized recommendation method for ideological and political resources needs to be designed based on reinforcement learning and evolutionary computing. In essence, a learner model has been formulated for the personalized recommendation of ideological and political resources. By harnessing reinforcement learning and evolutionary computing techniques, an algorithm tailored for personalized recommendations of these resources has been devised. Consequently, a personalized recommendation center for ideological and political resources has emerged, facilitating tailored suggestions to individuals. Experimental outcomes reveal that the proposed personalized recommendation approach, which incorporates reinforcement learning and evolutionary computing, exhibits a notable recommendation hit rate, average reciprocal ranking, and normalized cumulative gain, which proves that the designed personalized recommendation method for ideological and political resources has good recommendation effect, reliability, and certain application value.