Aiming at the problem of insufficient attack samples and traffic characteristics in network intrusion detection, a CNN-BiLSTM network intrusion detection method based on self-supervised feature enhancement was proposed to detect abnormal network traffic in traffic data. By analyzing the difference in the distribution of traffic characteristic, IQR outlier processing method was used for data preprocessing, and autoencoder was used to enhance the number of attack samples. A semi-self-supervised model composed of CNNBilSTM neural network and autoencoder was constructed to extract high-dimensional traffic characteristics and self-supervised features respectively. The combined features are input into the classification model as the final features for prediction and classification, so as to realize the function of network intrusion detection. The experimental results show that compared with other intrusion detection methods, the accuracy and F1 score of the proposed method are 85. 7% and 85. 1% respectively, which can effectively improve the detection accuracy of network intrusion and the detection ability of unknown attacks.