In view of the inefficiency of the artificial material classification method used by traditional textile production plants, it is difficult to meet the needs of large-scale textile production. Artificial intelligence and computer vision advanced technologies were applied to textile material classification, and a textile material classification method based on DSCI-Yolov8 was proposed. On the basis of the Yolov8 classification model, the ability of the model to extract the features of textiles at different scales is enhanced by adding the coordinate information attention module, and the distribution offset convolution is added to the c2f network module to achieve lower memory usage and higher computing speed. Experimental results show that compared with the Yolov8 network model, the accuracy of the proposed model is increased by 2.09 percentage points, and the detection speed is increased by 24.9%.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.