Scene perception and classification of FLIR images is a key technology in target recognition and of great significance to infrared reconnaissance and guidance. To resolve the problem of scene perception and classification of FLIR images， this study proposes a multi-label infrared image classification algorithm based on weakly supervised learning. First， a multi-label image classification technique is applied to FLIR images， and the images of multiple scenes are annotated using weakly supervised techniques. Infrared image features are extracted using the ResNet-50 network with a residual structure. Second， a CSRA module is introduced to capture the different spatial regions occupied by different classes. The CSRA module can improve the feature expression performance and realize the inference calculation of topological relationships between multiple labels. Finally， the advanced loss function ASL is introduced to solve the imbalance of the number of positive and negative labels in multi-label classification. The advanced loss limits the contribution of negative samples to the loss function and focuses attention on the positive samples during training. An experiment shows that the algorithm has good adaptability and accuracy， and the accuracy can exceed 90%. The algorithm can be used to perform multi-label classification with high accuracy and adaptability.