As network security issues become increasingly severe, the complexity and concealment of network attacks continue to increase, and the need for a reliable network intrusion detection system (NIDS) is imminent. This study is dedicated to solving this problem and proposes a unique NIDS based on neural networks. The Transformer encoder is integrated into the traditional neural network architecture, and a convolutional neural network (CNN) is applied to extract features, building a model that can process complex network traffic data more efficiently. After a series of rigorous experimental verifications, the model shines in performance. In terms of key indicators such as accuracy, recall, and precision, it significantly surpasses traditional models and other common neural network models. Regardless of the network bandwidth, attack frequency, or data set size, it shows excellent adaptability and stability. Especially when dealing with class imbalanced data sets, the model's detection ability for minority attacks has been effectively improved, providing a more solid guarantee for network security protection and injecting new vitality into the development of network intrusion detection technology.