针对传统文化图案分割模型存在边缘贴合精度低的问题，本文从模型预测和数据标签两个角度对分割模型进行优化。首先，提出了一种基于边缘预测的迭代上采样策略，在生成预测图的阶段，将预先训练的点分类器与网络浅层特征进行融合，进一步对模糊边缘的像素点进行分类，从而得到具有更高边缘质量的预测图。其次，针对像素标注存在标签模糊或错误问题，提出了一种基于标签松弛的混合损失函数，并与交叉熵损失函数相结合，支撑分割模型的训练过程。最后，在传统文化图案数据集上，仿真验证了本文所提算法的有效性，同时也证明了算法具有较强容错机制，可较好地提升分割质量。

Aiming at the problem of low edge fitting accuracy in traditional cultural pattern segmentation models, this paper optimizes the segmentation model from the perspectives of model prediction and data labeling. Firstly, propose an iterative upsampling strategy based on edge prediction. In the stage of generating the prediction map, the pre-trained point classifier is fused with the shallow features of the network, and the pixels of the blurred edge are further classified, so as to obtain a better prediction map with high edge quality. Secondly, for the problem of label ambiguity or error in pixel labeling, we propose a hybrid loss function based on label relaxation, which is combined with the cross-entropy loss function to support the training process of the segmentation model. Finally, on the traditional cultural pattern data set, we simulate to verify the effectiveness of the algorithm proposed in this paper, and also proves that the algorithm has a strong fault-tolerant mechanism, and can better improve the segmentation quality.