Aiming at the problem of uneven distribution and limited coverage of ideological and political education resources for college students, a PSO algorithm based method for optimizing the allocation of ideological and political education resources for college students is proposed. By using the ID3 algorithm to deeply explore and classify the multidimensional attributes of ideological and political education resources for college students, including resource types, content depth, audience preferences, and expected effects, a comprehensive resource feature library is constructed, providing a solid data foundation for subsequent allocation decisions. Designed and implemented a customized PSO algorithm framework that fully considers multiple factors such as resource scarcity, diversity of student needs, and maximization of educational outcomes. By simulating the search behavior of particle swarm optimization in the solution space, the resource allocation scheme is iteratively optimized until the optimal solution that satisfies all preset conditions or approaches the optimal solution is found, achieving fast convergence. Test results demonstrate that this method effectively and rationally distributes these resources, ensuring minimal disparities across varying request conditions, thus exhibiting a noteworthy advantage in terms of coverage.