Strategic Application of Deep Learning Methods in Global Educational Collaboration

In the context of globalization, educational cooperation has become an important way to solve the problems of unbalanced educational resources and differences in educational quality. Deep learning technology has great potential for application in the field of education, but its strategic value in global education cooperation has not yet been fully explored. The purpose of this paper is to explore how deep learning can empower global educational cooperation and address key issues such as optimal allocation of educational resources, cross-language and cross-cultural adaptation, and promotion of educational equity. A deep learning-based cross-language educational resource recommendation system (CLERS) is proposed, a deep learning-driven global education cooperation framework is constructed, cross-language recommendation algorithms and multicultural adaptation modules are designed, and its performance is evaluated through experimental validation and case studies. The experimental results show that CLERS significantly outperforms traditional and existing deep learning methods in terms of recommendation accuracy, user satisfaction and resource utilization. The case study further validates the effectiveness of its practical application in transnational education cooperation programs. This paper provides theoretical support and practical reference for the application of deep learning technology in global educational cooperation, which promotes the globalization of sharing and efficient use of educational resources.