Infrared image target recognition is of great significance to criminal investigation, but the resolution of criminal cases demands high requirements in terms of time and confidence coefficient. It is of great research value to design a lightweight target recognition algorithm of infrared criminal detection image to maintain excellent recognition accuracy and high recognition speed at the same time. Therefore, the excellent characteristic of biological immunity is drawn on and the immunogenic deep neural network algorithm is designed. The algorithm constructs innate immune network and adaptive immune network to extract image features. Then an immunogenic network enhancement algorithm is implemented to adjust the priority between different channels when processing image feature mapping, so as to improve the accuracy and speed of the algorithm. Experiments show that the proposed algorithm can effectively realize the fast and accurate recognition of infrared criminal detection images. When compared with VGG16, VGG19, Resnet34, Resnet50, MobilenetV2 and other models, the proposed algorithm not only achieves the highest test accuracy of 994%, but also has the fastest recognition speed.