<p>Home textile images have small inter-class differences and large intra-class differences, making home textile image retrieval face great technical challenges. In this paper, we design an effective retrieval model for home textile images, in which ResNet50 is used as backbone network, and a hybrid maximal pooling spatial attention module is proposed to fuse local spatial information at different scales, thus focusing on key information and suppressing irrelevant information. Moreover, we propose a new loss function called SD-arcface for fine-grained feature recognition, which adopts dynamic additive angular margin to improve the intra-class compactness and the inter-class separation of home textile images. In addition, we set up a large-scale dataset of home textile images, which contains 89<i>k</i> home textile images from 12<i>k</i> categories, and evaluate the image retrieval performance of the proposed model with two metrics, Recall@k and MAP@k. Finally, the experimental results show that the proposed model achieves a better retrieval performance than other models.</p>