In recent years， Deformable Convolutional Network （DCN） has been widely applied in fields such as image recognition and classification. However， research on the interpretability of this model is relatively limited， and its applicability lacks sufficient theoretical support. To address these issues， this paper proposed an interpretability study of DCN and its application in butterfly species recognition model. Firstly， deformable convolution was introduced to improve the VGG16， ResNet50， and DenseNet121 （Dense Convolutional Network121） classification models. Secondly， visualization methods such as deconvolution and Class Activation Mapping （CAM） were used to compare the feature extraction capabilities of deformable convolution and standard convolution. The results of ablation experiments show that deformable convolution performs better when used in the lower layers of the neural network and not continuously. Thirdly， the Saliency Removal （SR） method was proposed to uniformly evaluate the performance of CAM and the importance of activation features. By setting different removal thresholds and other perspectives， the objectivity of the evaluation is improved. Finally， based on the evaluation results， the FullGrad （Full Gradient-weighted） explanation model was used as the basis for the recognition judgment. Experimental results show that on the Archive_80 dataset， the accuracy of the proposed D_v2-DenseNet121 reaches 97.03%， which is 2.82 percentage points higher than that of DenseNet121 classification model. It can be seen that the introduction of deformable convolution endows the neural network model with the ability to extract invariant features and improves the accuracy of the classification model.