Aiming at the poor segmentation effect caused by the large scale difference of objects, uneven spatial distribution of samples, fuzzy boundary of objects and large span of scene area, this paper proposes a high-precision remote sensing building segmentation algorithm enhanced by integrating dynamic features. Firstly, the New_GhostNetV2 network is constructed, and the adaptive context-aware convolution is used to improve the algorithm's ability to capture the features of the sample space. Secondly, multi-level information enhancement modules are designed using ghost convolution combined with skip connections and feature branching strategies to enhance the feature integration. Then CGA (cascaded group attention) is introduced to enhance the adaptability of the model to diverse ground object forms through the calculation of independent attention within the group. Finally, the feature fusion module is constructed by the dynamic depth feature enhancer to further enhance the ability of model capture. The experimental results on the WHU data set show that the improved algorithm is 8.57% higher than the baseline model F1-Score and 12.48% higher than mIoU. Compared with other mainstream semantic segmentation models, the improved DeepLabv3+ has better segmentation accuracy.