Pine wood nematode disease is a devastating pine disease that poses a great threat to forest ecosystems. The use of remote sensing methods can achieve macroscopic and dynamic detection of this disease; however, the efficiency and accuracy of traditional remote sensing image recognition methods are not always sufficient for disease detection. Deep convolutional neural networks (D-CNNs), a technology that has emerged in recent years, have an excellent ability to learn massive, high-dimensional image features and have been widely studied and applied in classification, recognition, and detection tasks involving remote sensing images. This paper uses Gaofen-1 (GF-1) and Gaofen-2 (GF-2) remote sensing images of areas with pine wood nematode disease to construct a D-CNN sample dataset, and we train five popular models (AlexNet, GoogLeNet, SqueezeNet, ResNet-18, and VGG16) through transfer learning. Finally, we use the “macroarchitecture combined with micromodules for joint tuning and improvement” strategy to improve the model structure. The results show that the transfer learning effect of SqueezeNet on the sample dataset is better than that of other popular models and that a batch size of 64 and a learning rate of 1 × 10−4 are suitable for SqueezeNet’s transfer learning on the sample dataset. The improvement of SqueezeNet’s fire module structure by referring to the Slim module structure can effectively improve the recognition efficiency of the model, and the accuracy can reach 94.90%. The final improved model can help users accurately and efficiently conduct remote sensing monitoring of pine wood nematode disease.