Infrared-visible person re-identification has been widely used in video surveillance, intelligent transportation, security, and other fields. However, due to the differences between different image modalities, it brings great challenges to this field. The existing methods mainly focus on mitigating the differences between modes to obtain more discriminating features, but ignore the relationship between adjacent features and the influence of multi-scale information on global features. Here, a infrared-visible person re-identification method (MFANet) based on multi-feature aggregation is proposed to solve the shortcomings of existing methods. Firstly, the adjacent level features are fused in the feature extraction stage, and the integration of low-level feature information is guided to strengthen the high-level features and make the features more robust. Then, the multi-scale features of different receptive fields of view are aggregated to obtain rich contextual information. Finally, multi-scale features are used as a guide to strengthen the features to obtain more discriminating features. Experimental results on SYSU-MM01 and RegDB datasets show the effectiveness of the proposed method, and the average accuracy of SYSU-MM01 dataset reaches 71.77% in the most difficult all-search single-shot mode.