As a traditional form of Chinese art painting, Ink painting is the most important type of art painting research. However, due to differences in color and strokes, ink painting often faces issues in brushwork analysis, such as poor segmentation results and insufficient accuracy in segmentation and localization. Therefore, in order to achieve segmentation of brushwork in ink painting and improve the segmentation effect of ink painting, a digital cultural entropy regression based brushwork segmentation method for ink painting is proposed. The new method uses a digital cultural entropy regression model to locate the information entropy of ink painting. Meanwhile, the new model uses an improved mask area Convolutional Neural Network (CNN)to achieve image segmentation and contour localization of ink painting brushwork. Finally, a brand new segmentation system is built based on actual application situations. In the intersection to union ratio test, the digital cultural entropy model achieves the highest pixel accuracy of 92.34% and 94.64% in position and pixel analysis, respectively. Finally, in terms of system efficiency, the digital cultural entropy system can achieve a maximum of 88IPS, reducing instruction time by 0.91ms compared to other models. The response time is also shortened by 1.7ms. Therefore, the model developed in this study can more effectively achieve the segmentation of ink painting brushwork. This has great application value in improving the segmentation effect of brushwork in ink painting.

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