Design of Intelligent Question Bank System for Advanced Mathematics Based on Deep Learning Algorithm

This study aims to design an intelligent question bank system for advanced mathematics based on deep learning algorithms. By implementing core modules such as question generation, classification and retrieval, learning behavior analysis, and teacher assistance, it seeks to enhance the intelligence level of mathematics teaching and learning. The research employs Generative Adversarial Networks (GANs) and Diffusion Models (Diffusion Model) to dynamically generate questions, combining knowledge graphs with Transformer models to achieve precise matching of knowledge points. Factorization Machines (FM) are introduced to model student answering behaviors, constructing a dynamic assessment model for knowledge mastery based on Bayesian networks. A microservices architecture and distributed storage technology are adopted to ensure the systems stability and scalability in high-concurrency scenarios. The results show that the core functional modules of the system meet the expected goals in terms of accuracy of question generation, retrieval efficiency, precision of behavior evaluation, and effectiveness of teaching suggestions. The generated mathematical questions exhibit excellent diversity and coverage of knowledge points, while classification and retrieval, as well as teaching assistance functions, significantly improve teaching efficiency and personalized learning outcomes..