Application research on classification and integration model of innovation and entrepreneurship education resources based on GNN-PSO algorithm

With the development of innovation and entrepreneurship education, the classification and integration of educational resources have become the key to improving education quality. However, traditional methods cannot deal with complex and multi-dimensional educational resource data. Therefore, this study proposes a classification and integration model of innovation and entrepreneurship education resources based on GNN-PSO (graph neural networks and particle swarm optimization). The model uses the powerful feature extraction ability of GNN to dig deep into the internal relationship between educational resources. At the same time, it optimizes the classification and integration process with the help of the PSO algorithm's global search advantage. In the experimental link, we constructed an experimental dataset containing 10,000 innovation and entrepreneurship education resources, covering multi-dimensional information such as courses, cases, and teachers. Through comparative experiments, the GNN-PSO model's classification accuracy reached 92.5%, 15.3 percentage points higher than that of the traditional machine learning model. Regarding resource integration efficiency, the processing time of the GNN-PSO model is shortened by 40%, which significantly improves the management efficiency of educational resources. In addition, this study explores the application effect of the model under different educational resources. It finds that the GNN-PSO model has good generalization ability and scalability. The experimental results confirm that the classification and integration model of innovation and entrepreneurship education resources based on the GNN-PSO algorithm improves classification accuracy and optimizes the resource integration process, providing strong support for the development of innovation and entrepreneurship education.