With the popularization of emerging technologies such as 5G and IoT, network attacks present the characteristics of cross-platform penetration, enhanced covertness, and intelligent attack chain. Aiming at the problems of insufficient feature characterization ability and weak dynamic adaptability faced by the traditional rule-base-based intrusion detection methods, combining with deep learning, The architecture diagram of the network intrusion detection model was established based on the concept of network intrusion detection. The four challenges faced by current typical deep learning-based models—such as CNN(convolutional neural network), RNN(recurrent neural network), GAN(generative adversarial network), and AE(autoencoder)—were summarized and analyzed. Accordingly, the future development direction was analyzed and prospected.