In practical applications of image recognition, the training data tend to follow a long-tailed class distribution without regard to manual balancing.The recognition effect of long-tailed image recognition algorithms based on deep learning is not good, and the recognition accuracy of middle and tail categories is unsatisfactory.In order to address the problems, a Personalized Multi-expert Recognition Algorithm (PMRA) is proposed.Firstly, a multi-expert network is constructed by integrating multiple branches based on the residual network.Then, a personalized learning module is built by assigning personalized training data to different experts to improve the recognition accuracy of middle and tail categories, and a personalized information enhancement module is built by fusing the experts information to deal with the lack of information of middle and tail categories.In the multi-expert network fusing multiple modules, the overall recognition accuracy of long-tailed images is improved by two stages of learning.Finally, the experimental results on benchmark datasets of CIFAR-10-LT, CIFAR-100-LT, ImageNet-LT and iNaturalist2018 show that the recognition accuracy of the proposed algorithm on multiple datasets is better than that of other algorithms.