In order to further improve the accuracy of network anomaly detection, based on the analysis of existing intrusion detection methods, this study proposes a network packets intrusion detection method based on Convolutional Neural Networks (CNN) and Support Vector Machine (SVM). The method first preprocesses the data into a two-axis matrix. In order to prevent the algorithm model from over-fitting, the permutation function is used to randomly shuffle the data, and then the CNN is used to learn the effective features from the pre-processed data. Finally, this method uses SVM classifier to classify the vectors. In the dataset selection, we use the authoritative dataset commonly used in network intrusion detection—Kyoto University honeypot system dataset. This method proposed in this study is compared with the existing models with high detection rates, such as GRU-Softmax and GRU-SVM. The model has improved the highest accuracy by 19.39% and 12.83% respectively, which further improves the accuracy of network anomaly detection. At the same time, the method has greatly improved the training speed and test speed.