The purpose of text-image person re-identification is to employ the text description to retrieve the target persons in the image database. The main challenge of this technology is to embed image and text features into common potential space to achieve cross-modal alignment. Many existing studies try to adopt separate pre-trained unimodal models to extract visual and text features, and then employ segmentation or attention mechanisms to obtain explicit cross-modal alignment. However, these explicit alignment methods generally lack the underlying alignment ability needed to effectively match multimodal features, and the utilization of preset cross-modal correspondence to achieve explicit alignment may result in modal information distortion. An implicit multi-scale alignment and interaction for text-image person re-identification method is proposed. Firstly, the semantic consistent feature pyramid network is employed to extract multi-scale features of the images, and attention weights are adopted to fuse different scale features including global and local information. Secondly, the association between image and text is learned using a multivariate interaction attention mechanism, which can effectively capture the corresponding relationship between different visual features and text information, narrow the gap between modes, and achieve implicit multi-scale semantic alignment. Additionally, the foreground enhancement discriminator is adopted to enhance the target person and extract purer person features, which is helpful for alleviating the information inequality between images and texts. Experimental results on three mainstream text-image person re-identification datasets of CUHK-PEDES, ICFG-PEDES and RSTPReid show that the proposed method effectively improves the cross-modal retrieval performance, which is 2%-9% higher than the Rank-1 of SOTA algorithm.