With the development of biometric recognition technology, the research of tactile pressure footprint classification has been more and more frequently used, and the traditional classification method is labor-intensive. For tactile pressure footprint classification, the paper proposed a tactile pressure footprint classification method of fusion distribution graph network. Firstly, the convolutional features of the tactile pressure footprint image was extracted through the embedding module and the sample correlation matrix was obtained with the norm-regularization method. Then the fusion correlation matrix between the samples and the label one-hot vector was formed, and feature information was added through the self-attention module. A new feature distribution graph was obtained through the distribution module, and an association matrix between the labeled sample and the unlabeled sample was constructed. Finally, the convolution feature and feature distribution graph of the tactile pressure footprint image were used as the input of the update module to implement the tactile pressure footprint classification. The experimental results show that, compared with the few-shot classification method, this new methods classification accuracy of 5-way1-shot experiments on Mini-Imagenet and Tiered-Imagenet data set reached 71.71% and 74.34%, respectively. Meanwhile, the 5-way 1-shot and 5-way 5-shot experiments on the left and right foot data sets of tactile pressure reached 88.87% and 98.66%, respectively.