Aiming at the problem of low detection accuracy in the field of network intrusion detection, we study the intrusion detection model when there are few samples of anomalous traffic and the performance of classifiers is poor, and propose an intrusion detection model based on improved generative adversarial network and hybrid spatio-temporal neural network. The improved generative adversarial network generates artificial anomalous traffic samples with specific labels by learning the distribution characteristics of the anomalous traffic samples; the fusion convolutional neural network and bidirectional long and short-term memory neural network extracts the spatio-temporal fusion features of the attacking traffic, and utilizes the attention mechanism to weight the spatio-temporal fusion features and constructs a hybrid spatio-temporal neural network to classify and predict the network traffic. Simulation experiments of the proposed model are conducted on the UNSW-NB15 dataset, and the accuracy and F1 score are 92.93% and 94.81%, respectively, indicating that the proposed model can effectively improve the problem of category imbalance in the original dataset, and improve the detection capability of abnormal traffic samples and the detection accuracy of network intrusion.