Lanzhou lily is the only kind of sweet lily in China and it is one of the famous specialties of Gansu Province. However, its yield and quality were decreased significantly in recent years due to gray mold disease, bulb rot disease and other diseases and insect pests. In order to improve the antiinterference ability of Lanzhou lily diseases diagnosis model, the three full connection layers of VGG-16 convolutional network was replaced with capsule network module to construct convolutional capsule network. And the effects of capsule size and route iteration times on training time and model accuracy were analyzed systematically. The result of the experiment showed that the diagnosis accuracy of Lanzhou lily diseases via convolutional capsule network was 9920% when the capsule size was 8 and the route iteration time was 3. And the capsule size and the number of routing iterations had no significant effect on the accuracy of the model. In addition, the accuracy of VGG-16 model was slightly higher than that of convolutional capsule network when the affine transformation grade was 0.04~0.08. But the antiinterference ability of convolutional capsule network was obviously better than that of VGG-16 model for Gaussian noise, saltandpepper noise, speckle noise and other grades of affine transformation. So it was possible to use the convolutional capsule network for dealing with the realworld examples of Lanzhou lily diseases recognition. 