Design and Implementation of a Higher Education Management Information System based on SSH Framework with Bi-Layered Deep Networks

Higher Education needs powerful systems to handle academic, administrative, and performance data. Old systems have poor scalability and no predictive value to facilitate real-time decision-making. In this paper, the design and implementation of a higher education management system based on the SSH (Spring, Struts, Hibernate) framework and a Bidirectional Long Short-Term Memory (BILSTM) model are discussed. The SSH architecture allows for modular and efficient development, data handling, and scalability, and the BILSTM model examines student behaviour and forecasts academic patterns through historical and future sequence learning. The system supports attendance tracking, performance prediction, and course suggestion. The suggested framework was trained by historical academic records and performance metrics such as accuracy, precision, recall, and response time are evaluated. With respect to current systems, this solution enhances predictive accuracy by with the value of 98.6 %. With a combined architecture, the solution offers an intelligent, responsive, and scalable solution for managing higher education.