Design of Multimedia Teaching Software Based on Distributed Network Architecture

With the leap of network technology and the vigorous development of online teaching, many universities are actively adopting online means to optimize teaching and course management. This paper focuses on building an efficient and comprehensive auxiliary teaching platform that integrates functions such as student learning monitoring, course management, online testing, and teacher–student interaction, aiming to improve the quality of education. Designs based on distributed B/S architecture and web technology to ensure efficient resource allocation and expansion, meet the needs of large-scale concurrent learning, and achieve cross-platform access to enhance user experience. The platform features personalized learning support, enhanced interactive collaboration, and the construction of a multimedia teaching database through data analysis. The innovation lies in using neural network technology to create an intelligent question answering module, utilizing cosine similarity to automatically group teaching resources, and using graph convolution and variational autoencoder techniques to construct a student performance monitoring model. Experimental verification shows that the platform runs stably, effectively reduces the blocking rate, and significantly improves students’ academic performance, pass rate, and learning interest. This design not only successfully completed the teaching task, but also provided valuable experience for the application of online-assisted teaching systems in subject education.