UAV (Unmanned Aerial Vehicle) image recognition in foggy conditions is crucial in environmental monitoring, disaster rescue, and other fields. However, owing to light attenuation and fog-obscuring ground objects in foggy environments, the conventional single-feature recognition method for UAV foggy images is ineffective. Hence, this study proposes a method that combines multi-feature UAV foggy-image recognition. The dark channel features, texture features, and color features in UAV images are extracted, and the extracted features are combined into feature vectors and subjected to dimensionality reduction. Finally, they are trained and classified using a support vector machine to achieve accurate recognition of UAV foggy images. The experiment demonstrates that the method achieves an accuracy of 97.68% and a false-alarm rate of 5.05% on the UAV foggy-image dataset, thus highlighting its superiority over four other compared methods. This method provides a new reliable solution for the image recognition and defogging of UAVs in foggy environments, as well as offers high practicality and popularization value.