Panoramic segmentation is an important research direction in computer vision.Considering that different application scenarios have different requirements for semantic segmentation accuracy,a semantic enhancement loss function and panoramic segmentation method based on attention mechanism is proposed in this paper.Firstly,the semantic categories are grouped according to their importance,and the attention mechanism is added to distinguish different semantic information,and the classification imbalance is effectively suppressed through the design of loss weight.Secondly,a panoramic segmentation network is designed using MaskR CNN network as the instance segmentation sub branch and adding FPN structure as the semantic segmentation benchmark to improve the segmentation accuracy of the required object types.Finally,the overlapping problem of instance and semantic segmentation branch output in network structure is avoided by designing overlapping result elimination rules.The comparative experiments on COCO data sets show that the semantic enhancement loss function proposed in this paper effectively improves the segmentation effect of semantic categories with higher priority,and provides more high quality semantic information for panoramic segmentation of different application scenarios.