A Deep Learning and Data Mining-based Optimization Method for Shared Mechanisms of Digital Economy Factors in Government-University-Enterprise Collaboration

With the rapid development of the digital economy, resource sharing and optimal allocation have become key issues to promote efficient economic operation. Especially under the framework of government-school-enterprise collaboration, how to improve resource sharing efficiency and reduce costs through intelligent means has become an important issue that needs to be solved urgently. Traditional resource optimization methods face problems such as slow convergence speed, poor stability, and low computational efficiency, and they urgently need to be improved with the help of advanced technology.This study proposes a resource sharing optimization framework based on a combination of deep neural network (DNN) and association rule mining. In this framework, we use the Apriori algorithm to mine association rules in historical data and use them as input features of the deep learning model to optimize resource allocation strategies. Experiments show that deep learning methods have significant advantages in improving resource sharing efficiency, optimizing costs, and improving resource allocation fairness. Specifically, after the optimization of the model, the resource sharing efficiency increased by 20 percentage points, the cost was reduced by 15%, and the fairness of resource allocation was significantly improved.