Design of a College Student Management Platform Based on Embedded Neural Network Algorithm for Network Security Technology

With the rapid development of computer science and network technology, the education system is undergoing unprecedented changes, and the scale of universities continues to expand, which poses more stringent challenges to building an efficient and intelligent campus network environment. In this context, this study focuses on exploring optimization strategies for student management in the context of online culture, particularly by integrating embedded networks and data analysis technologies to deeply analyze new dimensions of learning status monitoring. We innovatively apply an anomaly detection mechanism based on embedded networks to conduct a detailed analysis of students' learning status. By constructing a complex learning behavior model based on embedded networks, the system can intelligently identify students with abnormal learning states and achieve precise control over their learning status. The system is deployed in a Windows environment and integrates high-performance databases to meet and efficiently manage the needs of massive data. The system has specially designed an administrator login interface, which is intuitive and easy to operate, allowing administrators to quickly access and query student management information. These information collected and analyzed with the support of embedded network technology greatly assist university educators in making scientific decisions and efficiently executing student management work.