To address the problem that infrared video lacks texture detail features which is difficult to balance the computational complexity and recognition accuracy in human behavior recognition, a global bilinear attention-based behavior recognition method for infrared video is proposed in this paper. Firstly, in order to efficiently compute human behavior in infrared video, a joint extraction module based on a two-stage detection network is designed to obtain human joint point information, and the resulting 3D heat map of joints is innovatively used as an input feature for the human behaviour recognition network in infrared video. Moreover, to further improve the recognition accuracy on the basis of lightweight computation, a global bilinear attention-based 3D convolutional network is proposed to enhance the attention from both spatial and channel dimensions modeling capability to capture global structural information. The experimental results on the InfAR and IITR-IAR datasets demonstrate the effectiveness of the method in infrared video behavior recognition.