Design and Development of an Intelligent Student Work Management Platform Using the B/S Model and FT Transformer Architecture

The research presents a sophisticated student work management system based on the B/S model to resolve inefficiencies in existing academic administrative processes with a view to optimizing data access, performance monitoring, and instant collaboration. The existing systems are prone to delayed data retrieval, low scalability, high maintenance expenses, and low adaptability to changing student requirements, whereas conventional solutions, which are usually client/server-based, encounter deployment difficulties and device dependence. The proposed model blends an FT Transformer framework into the B/S framework, facilitating high-dimensional data processing, contextual learning, and smart task scheduling. Compared to traditional management tools, it exhibits a gigantic performance jump with 98.8 % accuracy and 97.4 % F1-score, outperforming traditional frameworks by remarkable margins. The system's excellence is based on adaptive feature extraction, lower latency, cross-platform compatibility, and strong security features, facilitating smooth management of student records, assignments, and progress reports. Through accuracy-driven analytics and smart resource allocation, the model provides increased reliability, scalability, and decision-making support, making it an excellence for contemporary learning environments.