Attention mechanism is widely employed in Person Re-Identification task to allocate the weight of features. However, most of the existing attention-based methods focus on the region of interest but ignore other potential diverse information, which may cause a sub-optimal results in some situations. To alleviate the problem, we propose a novel Attention-Guided Multi-Clue Mining Network (AMMN). By leveraging the attention mechanism and the dropblock, the model can further emphasize the features other than the attention areas. All of the output features are finally grouped into a multi-clue representation contributed to person identities. Extensive experimental results demonstrate the proposed method outperforms current competitors of relevant methods on several benchmark datasets such as Market1501, DukeMTMC-reID, CUHK03. We also achieve state-of-the-art performance on Occluded datasets.