针对DeepLabv3+模型对图像目标边缘分割不准确、不同类目标分割不一致等问题，提出一种基于类特征注意力机制融合的语义分割算法。该算法在DeepLabv3+模型编码端先设计一个类特征注意力模块增强类别间的相关性，更好地提取和处理不同类别的语义信息。然后采用多级并行的空间金字塔池化结构增强空间之间的相关性，更好地提取图像不同尺度的上下文信息。最后在解码端利用通道注意力模块的特性对多层融合特征重新校准，抑制冗余信息，加强显著特征来提高网络的表征能力。在Pascal Voc2012和Cityscapes数据集上对改进模型进行了有效性和泛化性实验，平均交并比分别达到了81.34%和76.27%，使图像边缘分割更细致，类别更清晰，显著优于本文对比算法。

Aiming at the problems of inaccurate segmentation of image target edges and inconsistent segmentation of different types of targets by the DeepLabv3+ model, a semantic segmentation algorithm based on the fusion of class feature attention mechanism is proposed. The algorithm firstly designs a class feature attention module on the encoding end of the DeepLabv3+ model to enhance the correlation between categories, so as to better extract and process semantic information of different categories. Secondly, the multi-level parallel spatial pyramid pooling structure is used to enhance the correlation between spaces so as to better extract contextual information at different scales of images. Finally, at the decoding end, the characteristics of the channel attention module are used to recalibrate the multi-layer fusion features, and the redundant information is suppressed to strengthen the salient features to improve the representation ability of the network. The effectiveness and generalization experiments of the improved model are carried out on the Pascal Voc2012 and Cityscapes datasets, and the average intersection ratios reach 81.34% and 76.27%, respectively.The image edge segmentations in this paper are more detailed, the categories are clearer, and are significantly better than the compared algorithms.