Aiming at the problem that traditional visible light is difficult to realize personnel behavior detection and identity recognition in dark environment, this paper combined with infrared thermal imaging technology to study an algorithm for personnel behavior detection and identity recognition in dark environment based on Baidu Paddle deep learning framework. First, after field collection, the behavioral dataset of infrared thermal imaging personnel totaled 10 900 pieces of 9 behavior categories and the double-light face dataset totaled 3 000 pieces of 30 personnel. In terms of behavior detection, the lightweight network PP-LCNet is used to improve the YOLOv5 backbone network for personnel behavior detection, reducing model parameters greatly and improving detection accuracy and reasoning speed. In terms of face recognition, CycleGAN algorithm is introduced to improve InsightFace to transform infrared faces into visible faces for identity recognition and improve face recognition accuracy in dark environments. Finally, the cascade of infrared human behavior detection network and face recognition network is realized, and real-time behavior detection and identity recognition can be achieved in the dark environment, which has a good application effect. The experimental results show that compared with the original network model, the parameters of YOLOv5 based on PPLCNet are reduced by 56. 4%, the average precision mAP is increased from 89. 1% to 94. 7%, and the reasoning speed is increased from 68 to 101 fps. Based on CycleGAN algorithm, the recognition accuracy of InsightFace is improved from 84% to 99% in the dark environment of the original network.