Abstract: Intelligent identification of personnel in underground video monitoring in coal mines is of great significance for improving the efficiency of personnel supervision and reducing the occurrence of safety accidents. Affected by the complex underground environment and the performance limitations of monitoring video equipment, the underground video monitoring images have problems such as low resolution, occlusion and background interference, resulting in small differences among underground personnel and low accuracy of personnel re-identification. In order to solve the above problems, a network structure based on distance metric and channel attention is proposed. The structure is used for personnel re-identification in complex underground environments. In order to solve the problem that it is not easy to distinguish personnel from background in monitoring images, a channel attention module is introduced into the backbone network to make it pay more attention to the foreground characteristics of personnel and suppress the background information. Moreover, the size of the characteristic map output from the last layer of the backbone network is doubled so as to obtain more fine-grained characteristics, enrich the characteristic information of personnel and enhance the network's ability to learn characteristics. On the basis of realizing the classification of personnel with different identities, using the absolute distance information between the images of personnel, the distance metric module is used to sample and weight the personnel images who are difficult to identify, increase the weight of the difficult samples in the back propagation, and make the network pay more attention to the discriminative personnel characteristics. The identity loss and distance metric loss are jointly used to optimize the characteristic layer, so that the network can extract more discriminative personnel characteristics to improve the re-identification accuracy. The Miner-CUMT data set is used to verify the proposed method for personnel re-identification in complex underground environments. The results show that the method can make full use of the key information of personnel with different identities in the underground, so that the identification network has stronger discrimination ability and improves the accuracy of personnel identification in the underground.