The traditional way of resource allocation often lacks scientific and systematic, and is difficult to adapt to the needs of modern education development. The rapid development of artificial intelligence technology provides new technical means and solutions for the optimal allocation of educational resources, and multi-dimensional and multi-objective resource allocation optimization can be realized through intelligent algorithms to improve the efficiency and quality of education. This study constructs a resource allocation optimization model for innovation and entrepreneurship education based on multi-objective particle swarm algorithm, and evaluates the resource allocation efficiency through DEA method and Malmquist index model. The study establishes an evaluation system containing input indicators such as human resources, material resources and financial resources, and output indicators such as talent cultivation, scientific research and social services, and uses the multi-objective particle swarm algorithm to solve the resource allocation optimization problem. Taking 10 colleges and universities in G city as the research object, the BCC model and Malmquist index are used to analyze the innovation and entrepreneurship resource allocation efficiency statically and dynamically from 2018 to 2022. The results show that the HV value of this paper’s algorithm is 0.56225, which is better than 0.55219 of SPEA2DE and 0.53897 of NSGAIII; the average value of the comprehensive efficiency of innovation and entrepreneurship of the universities in G city in 2018-2022 is 0.902; 3 out of 10 universities reach DEA effective, with a ratio of 30%; and the average value of the index of the total factor productivity change is 0.999.The study shows that the multi-objective particle swarm algorithm has good performance in the optimization of innovation and entrepreneurship education resource allocation, and can provide scientific support for the decision-making of resource allocation in colleges and universities.
