Design of a Machine Learning-Based Information Integration System for Local Applied Undergraduate Colleges

Abstract

This paper designs an educational information integration system based on machine learning, aimed at enhancing management efficiency and decision-making quality in local applied undergraduate colleges. The system employs a layered architecture and microservice design, comprising four core functional modules: student information management, teaching resource management, teacher assessment, and intelligent decision support. Machine learning techniques are integrated into the design to enable functionalities such as student performance prediction and teaching resource optimization. The data model accommodates both relational and non-relational data to meet complex data processing needs in the education sector. The system architecture considers performance, reliability, and security, supporting high-concurrency access and big data processing. User interface design emphasizes interaction experience and operational efficiency. This design provides innovative ideas for educational information construction, with strong practical value and significance for promotion.