To address the challenges of high computational complexity， limited detail extraction， and fuzzy boundaries in the current DeepLabv3+ semantic segmentation network， this study proposes an enhanced DeepLabv3+ model incorporating attention mechanisms. Specifically， the lightweight MobileNetV2 is employed as the backbone to balance high representational capacity with a significant reduction in model parameters. A parameter-free lightweight attention mechanism （SimAM） is integrated into the low-level features of the backbone network to prioritize key features and enhance feature extraction capabilities. Furthermore， the global average pooling in the ASPP module is replaced with Haar Wavelet Transform Downsampling （HWD） to preserve spatial information. An External Attention Mechanism （EANet） is also introduced after the ASPP module to leverage contextual information and achieve multi-scale feature fusion， thereby improving semantic understanding and segmentation accuracy. Experimental results demonstrate that the proposed model achieves a 2.82% improvement in mean Intersection over Union （mIoU） on the VOC2012 dataset compared to the original DeepLabv3+ model. This research enhances the precision of semantic segmentation and offers novel insights for advancing applications in computer vision.