In the era of big data, cloud computing, and machine learning, this has become essential to promote the better development of ideological and political education (IPE) in institutions and universities. In fact, we must pay close devotion to the integration and utilization of online teaching resources, take full benefits of the assistances of big data, machine learning, and continuously collect and sort resources that are conducive to IPE in higher vocational academies, so as to optimize the educational process. In fact, the resource allocation within the context of the IPE is not well-addressed in the existing literature; and the allocation of resources is quite unreasonable. In higher vocational education, the form and content of the IPE will enhance its effectiveness. In this paper, we use the ant colony algorithm to efficiently obtain the solution set for resource allocation, thereby addressing the issues of unreasonable allocation of IPE resources and inefficient testing. In addition, the local search method is incorporated into the ant colony optimization technique to perform a local search on the solution set of the obtained resource allocation in order to increase the algorithm’s performance. On the standard test set, algorithm comparison experimentations are carried out to validate the efficacy and efficiency of the suggested algorithm.