With the popularization of digital applications, network security issues are also receiving increasing attention. The security issue of educational digital networks has attracted much attention. This article adopts a neural network-based method for anomaly detection in educational digital networks. A neural network model was constructed by analyzing and processing network data. First, we need to preprocess the network data, including Data cleansing, feature extraction and other steps. Then, the preprocessed data is used as input and trained and tested using a neural network model. During the training phase, the model will automatically adjust parameters to adapt to the features of the data, thereby achieving better recognition and classification results. After experimental verification, the neural network-based education digital network security and anomaly detection model proposed in this article can effectively detect and identify network abnormal behavior, improving the security of education digital networks.