In view of the problems of resource conflict, uneven student coverage, insufficient connection of ideological and political topics and feedback lag in the organization of campus cultural activities in colleges and universities, a dynamic scheduling of campus cultural activities and improvement of ideological and political education effectiveness under the framework of reinforcement learning was proposed. The model encodes student portraits, activity resources, time Windows, topic labels and feedback data into scheduling states, generates the combination scheme of activity time, venue, object and topic by the reinforcement learning strategy network, and balances the participation quality, resource fairness and education effectiveness through the multi-objective evaluation module. The experiment builds a simulation environment based on 148 candidate activities, 5200 students, and 320 time slices. The results show that the activity completion rate of the model in this paper reaches 96.42%, the scheduling conflict rate reduces to 4.18%, and the comprehensive score of ideological and political education effectiveness reaches 0.836, which is better than manual scheduling, static priority scheduling and heuristic greedy scheduling. The research provides a computable path for the intelligent organization of campus cultural activities and the optimization of ideological and political education process in colleges and universities.
