Abstract: The images from mountainous areas are of many miscellaneous types and are captured remotely by Unmanned Aerial Vehicles, making it difficult to accurately recognize the pine wilt disease. Aiming to solve this problem, this paper proposes a two-level fusion deep learning model with an attentional mechanism for the classification and recognition of pine wilt disease. Firstly, the VGG16 model is used to classify and “slim” the captured images. Then, the output images containing infected pine wilt disease from the first stage are inputted into the improved YOLOv5 object recognition model. The model further expands the receptive field by introducing the attention mechanism module so that the infected pine wilt disease can be accurately recognized. Finally, the proposed method is compared with other classical deep learning models. The results of comparative experiments show that the proposed two-level fusion deep learning model based on VGG16 and improved YOLOv5 has the best recognition effect with 85.58% recognition accuracy, which is higher than the other four two-level fusion deep learning models. The proposed approach can not only improve the accuracy but also solves the problem that manual image classification is needed before pine wilt disease is recognized.