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In view of the fact that the recognition effect of the feature extraction network at this stage is not ideal when the test samples are skewed, fuzzy, defective and other changes, improving the network performance by expanding, transforming, scaling and other ways of training samples cannot dynamically meet the problem of the actual complex disease image recognition task, In ResNet50, a global feature deep learning network (GFDL-Net) based on global feature extraction was designed by introducing a two-layer attention mechanism and channel feature extraction mechanism. The network included channel feature extraction sub network (Squeeze and exception net, SE-Net) and double feature extraction net (DFE-Net), the global feature extraction ability of the network was improved from two aspects: channel space feature extraction and plane key point feature extraction. In order to verify the effectiveness of GFDL-Net, tests such as rotation at different angles and color transformation were added to the images of 15 diseases such as pepper, potato and tomato. It was found that the average recognition accuracy was 20.05 percentage points, 18.62 percentage points and 21.97 percentage points higher than that of ResNet50, BoTNet and EfficientNet respectively after adding rotation to the samples. Compared with ResNet50, BoTNet and EfficientNet, the average recognition accuracy was 3.57 percentage points, 0.53 percentage points and 3.98 percentage points higher, and the recognition speed was 4.4 times, 4.9 times and 2.0 times of ResNet50, BoTNet and EfficientNet respectively after adding the shading, saturation and contrast transformations. The experiment proved that the improvement of GFDL-Net in the global feature extraction ability of images can effectively improve the generalization ability and robustness of the network, which can be used to solve the crop disease recognition task of changing samples.