There is a problem with incorrect performance positioning with the optimum allocation of resources, which is a major problem in university innovation and entrepreneurship programs. Traditional particle swarm algorithms fall short when it comes to teaching students about innovation and entrepreneurship since they can’t fix the problem of incorrect optimization and placement. This leads to the provision of an optimum allocation of resources for innovation and entrepreneurship education in higher education institutions based on the PSO algorithm and the evaluation of that allocation. Firstly, the indicators are divided according to the requirements of the optimum allocation of resources in order to reduce interference factors, and the influencing variables are discovered using the social behavior observation theory. After that, we apply the idea of social behavior observation to design an optimum resource allocation system using the PSO algorithm, and we look closely at the results. When compared to traditional Particle swarm algorithms, the PSO method achieves better outcomes in terms of accurate resource allocation and time spent affecting variables, according to MATLAB simulation findings. This is true under certain evaluation circumstances.