【Objective】As UAV technology continuously develops and the deep-learning theory is explored in-depth, the application of UAVs in forest patrol and forest fire monitoring has drawn increasing attention. In the current professional courses of electronic information such as image processing and computer vision, there is a lack of teaching cases related to small-target recognition. Also, in forest fire sample images, there are problems like hidden fire points and easy-to-be-missed detection. Thus, a deep-learning-based small-target forest fire detection model is proposed. 【Method】A lightweight backbone network was utilized. Moreover, a global attention mechanism was introduced in the feature fusion layer, which help to reduce information loss and enhance the performance of the deep neural network. Additionally, a small-target detection layer is to detect shallower feature maps, thereby achieving high-precision detection of small-target forest fires. The detection effect of the model was tested on different datasets and in multiple scenarios. 【Result】The results indicate that the constructed model attains 84.79% in the mAP index, which is a 4.45% improvement compared with the YOLOv5s model, and the FPS remains above 60. This verifies the rationality and effectiveness of the model in detecting small-target forest fires from the aerial photography angle. 【Conclusion】The model shows excellent performance in both detection accuracy and speed. Future research can explore semi-supervised or self-supervised learning to reduce data annotation costs while maintaining recognition accuracy.