In order to realize unmanned production in factories, textiles need to be sorted efficiently. The manual classification method for traditional textile production plants has the problem of low efficiency and difficulty in meeting the needs of large-scale production. Artificial intelligence and computer vision advanced technology were applied to textile material classification, and a textile material classification algorithm based on DSCI-YOLOv8 was proposed. On the basis of the original classification network of the YOLOv8 model, the coordinate information attention module is added to enhance the model′s ability to extract the features of textile materials at different scales, improve the accuracy of network classification, and reduce some of the calculations and parameters required for calculation. Secondly, the distributed offset convolution is added to the C2f network module, which improves the network structure of the classification neural part, so that the memory usage is reduced and the computation speed is improved. Experimental results show that the accuracy of the improved model is increased by 2.09 percentage points and 13.5% increase in image processing per second compared with the YOLOv8 model. While greatly reducing the calculation cost, it effectively improves the accuracy and speed of textile material classification. It can meet the testing needs of the textile industry for product category classification and quality.