Aiming at the problems of difficult dataset acquisition, insufficient samples, and low recognition accuracy in apple leaf disease image recognition, a disease recognition network based on multi-scale feature extraction ConvNext (M-ConvNext) model was proposed. A data enhancement method combining improved CycleGAN and affine transformation (CycleGAN-IA) was used. Firstly, the CycleGAN network structure was optimized by using a convolutional kernel with a smaller sensory field and a residual attention module, and a binary cross-entropy loss function instead of the mean-variance loss function of CycleGAN network, in order to generate high-quality sample images and improve the complexity of sample features;then affine transformation was applied to the generated images to improve the spatial complexity of the data samples, which solved the problem of insufficient data samples, and was used to assist the subsequent disease recognition model. Secondly, the M-ConvNext network was constructed, which was designed with the G-RFB module to acquire and fuse the feature information of each scale, and the GELU activation function enhanced the feature expression ability of the network to improve the accuracy of apple leaf disease image recognition. Finally, the experimental results showed that the CycleGAN-IA data enhancement method can play a good role in expanding the dataset, and it was verified on the commonly used network that the enhanced dataset can effectively improve the accuracy of apple leaf disease image recognition;through the ablation and comparison experiments, the recognition accuracy of M-ConvNex can be up to 99.18%, which was 0.41 percentage points more than the original ConvNext network, and 3.78 percentage points, 7.35 percentage points, 4.07 percentage points higher than that of ResNet50, MobileNetV3, and EfficientNetV2 networks, respectively, which provided an idea and laid a foundation for the subsequent recognition of crop diseases.