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Recognition of dorsal hand vein is an emerging biometric identification technology, which has obvious advantages compared with other biometrics, such as uniqueness, anti-counterfeiting, stability, and non-contact. Due to the difference of the acquisition equipment and acquisition environment, the gray-scale images of the dorsal hand vein have differences in brightness, angle rotation, scale scaling, etc., so recognition rate is low. Therefore, a dorsal hand vein recognition algorithm based on multi-image fusion and Xception network is proposed. Firstly, a binary texture map is obtained by segmentation after image preprocessing, and then the binary image is transformed into a distance map, and then the skeleton image is achieved through thinning of the binary image. Finally, the binary image, distance image, and skeleton image are combined to obtain a three-channel merged image containing texture features and shape features. The Xception architecture is used as the classification network, and its activation function ReLU is changed to the more nonlinear activation function h-swish. Relevant experiments are carried out on two databases, library 1 and library 2, built by our laboratory. Library 1 is used as training set and library 2 is used as test set. The recognition rate reaches 93.54%.