Person re-idetntification algorithms based on global features primarily use the cross-entropy loss function and triplet loss function to supervize network learning. However, the original triplet loss function does not optimize an intraclass distance and increases an interclass distance. To solve this problem, an improved person re-idetntification algorithm based on global features is proposed. The algorithm is improved on the basis of the triple loss function, that is, an intraclass distance is introduced into the original triple loss function, so that the improved triple loss can be reduced while increasing the interclass distance intraclass distance. A number of experiments have been conducted on the Market1501, DukeMTMC-reID, and CUHK03 datasets. The experimental results show that the proposed algorithm obtains discriminative features, and a model based on the global features can achieve a performance that approaches or even exceeds some local feature models.