随着生物特征识别技术的发展，触觉压力足迹分类的研究得到越来越多的应用，而传统的分类方法比较耗费人力。针对触觉压力足迹分类，本研究提出了一种融合分布图网络的触觉压力足迹分类方法。首先通过嵌入模块提取触觉压力足迹图像的卷积特征并采用范数正则化方法得到样本相关性矩阵，再将样本与标签one-hot向量构成融合相关性矩阵，通过自注意模块增加特征信息，经分布模块得到新的特征分布图，构建标记样本和未标记样本间的关联矩阵，最后将触觉压力足迹图像的卷积特征和特征分布图作为更新模块的输入，实现触觉压力足迹分类。实验结果表明，与小样本分类方法相比，本方法在Mini-Imagenet、Tiered-Imagenet数据集上的5-way1-shot实验分类准确率分别达到71.71%和74.34%，同时在触觉压力左右足数据集上的5-way1-shot和5-way5-shot实验分类准确率分别达到88.87%和98.66%。

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.