Research on Anmaly Detection Technology in Educational Big Data Based on Deep Neural Networks and Classroom Management Optimization

In the context of educational informatization, the rapid growth of educational big data has provided new data support for classroom management and educational decision-making. However, anomaly detection in this data still faces both technical and theoretical challenges. This paper explores the anomaly detection methods in educational big data based on deep neural networks from a theoretical perspective, analyzing the characteristics of educational data and the difficulties associated with detecting anomalies. By combining traditional anomaly detection models with deep learning methods, this paper outlines the theoretical advantages of deep neural networks in anomaly detection and discusses their potential applications in educational management. Finally, the paper proposes theoretical directions for further optimizing deep neural network models in the education sector, offering support for classroom management and educational optimization.