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Ink and wash paintings (IWPs) is the gem of Chinese traditional arts. While existing research for IWPs is primarily focused on image processing approaches, we propose a style-based algorithm in this paper to complete their automatic classification. As IWPs Images do not have colors or even tones, the proposed algorithm applies edge detection to locate the local region and strokes to enable histogram-based feature extraction and capture important events and changes in properties of painting strokes. Such features are then applied to drive a neural network to complete the automatic classification. Evaluation via experiments supports that the proposed algorithm achieves good performances, providing excellent potential for computerized analysis and management of IWPs.