Network intrusion detection has been widely discussed and studied as an important part of protecting network security. Therefore, this paper presents an in-depth study of the application of an improved V-detector algorithm in network intrusion detection. In this paper, we construct a V-detector intrusion detection model, adopt the “self-oriented” identification principle, and randomly generate detectors with large differences from the health library. A smaller number of detectors are used to compare the data information generated by the computer, and if they are similar, they are judged as intrusions. Intrusion detection experiments are performed on multiple types of networks by using classifiers to determine whether the access to be detected is an attack access. The experimental results show that the model has the lowest false alarm rate for mixed feature networks, with a false alarm rate of only 13% and a detection rate of 89%, with a sample size of 25,987. After the improvement of the V-detector intrusion detection model, the error correction output problem leads to a network intrusion with a miss rate of only 11% and a protection rate of 85%. The experimental data proved that the model has the advantages of large data size and comprehensive intrusion attack types.