Abstract: In order to solve the problem of unsatisfied accuracy with the simple styles of coarse-grained clothing image classification, a fine-grained clothing image classification method by style feature description is proposed for various fashion women clothing. Firstly, the part-based detection was conducted to detect the unclassified images combing the training dataset of fashion women clothing. Secondly, four kinds of low-level features including HOG, LBP, color histogram, and edge operator of the training images and the unclassified clothing images after part-based detection were extracted, which can respectively obtain the images after feature extraction. Then, the style feature description was defined to match the four low-level features, and the styles and attributes of fashion women clothing were obtained by the supervised learning using random forests and multi-class SVM. Finally, the fine-grained classification results were implemented and output. Experimental results show that the proposed method can accurately detect and classify various types of clothing images,and the classification accuracy and precision are improved greatly with the better practical applications.