Distributed Sharing and Personalized Recommendation System of College Preschool Education Resources Under the Intelligent Education Cloud Platform Environment

The emergence of Intelligent Education Cloud Platforms has revolutionized how educational resources are shared and utilized, enabling a more inclusive and adaptive approach to learning. In the context of college preschool education, the integration of distributed sharing and personalized recommendation systems addresses critical challenges in resource accessibility and learner engagement. Traditional methodologies often rely on static and generalized frameworks, lacking the flexibility to cater to diverse learning needs and the scalability for dynamic resource allocation. These limitations hinder their capacity to provide tailored educational pathways and real-time adaptability, which are essential in preschool education’s highly individualized context. To overcome these barriers, we propose a Knowledge-Adaptive Education Network (KAEN) augmented by the Adaptive Learning Pathway Strategy (ALPS), tailored for deployment on Intelligent Education Cloud Platforms. KAEN leverages graph-based knowledge representation, dynamic content alignment networks, and reinforcement learning to optimize resource recommendation and personalize learning experiences. ALPS complements this system by generating individualized learning pathways, integrating multi-modal content, and real-time feedback mechanisms to enhance engagement and educational outcomes. Experimental validation demonstrates significant improvements in resource utilization efficiency, learner engagement metrics, and adaptive content delivery quality. These findings underscore the potential of integrating AI-driven frameworks into Intelligent Education environments, offering scalable and effective solutions for preschool education resource sharing and personalization.