To inherit and innovate the traditional blue calico vein pattern,it i s necessary to classify the vein pattern of blue calico. Therefore,a method of vein pattern classification based on the improved convolu tion neural network model of VGGNet is proposed. Firstly,the original blue calico vein pattern images are captured,and the imag e samples are expanded by utilizing image enhancement technology to construct the training data set.Secondly,the classic VGGNet 16convolutional neural network structure is improved,the convolution group is added and the network parameters are adjus ted,and the Dropout layer is added.At the same time,this paper analyzes and verifies the effect of training optimization strat egy on the classification of the vein pattern for blue calico.Finally,using the image samples in the training set and validation set, the network model parameters are acquired through automatic learning,the optimal network model of vein pattern classification is acquired and the ideal classification results are obtained.The experimental results show that the improved convolutional neural n etwork model can classify 5types of vein patterns of blue calico,and the average classification accuracy is 89.73%,which provide s a new idea for the inheritance and innovation of blue calico vein patterns.