Narrowing the difference between modalities is always challenging in cross-modal person re-identification from images to texts. To address this challenge, this study proposes an improved method based on contrastive language-image pretraining-person re-identification (CLIP-ReID) by integrating a context adjustment network module and a cross-modal attention mechanism module. The former module performs a deep nonlinear transformation on image features and effectively combines with learnable context vectors to enhance the semantic relevance between images and texts. The latter module dynamically weights and fuses features from images and texts so that the model can take into account the other modality when processing the information of one modality, improving the interaction between different modalities. The method is evaluated on three public datasets. Experimental results show that the mAP on the MSMT17 dataset is increased by 2.2 % and R1 is increased by 1.1 %. On the Market1501 dataset, there is a 0.5% increase in mAP and a 0.1% rise in R1. The DukeMTMC dataset sees a 0.4% enhancement in mAP and a 1.2% increase in R1. The results show that the proposed method effectively improves the accuracy of person re-identification.