Objective Considering the problems of low segmentation accuracy and a large number of parameters in existing semantic image segmentation networks a semantic segmentation algorithm integrating lightweight and attention mechanisms was proposed. Methods The algorithm replaced the Xception backbone network of the original network model structure with the MobileNetV2 network based on the DeeplabV3+ network model structure and constructed a lightweight semantic segmentation network structure so as to reduce the number of model parameters and computational volume and improve the segmentation speed. Additionally an attention module mechanism was introduced during the encoding stage to effectively capture correct features of focused semantic information enabling the network to focus only on relevant points during the learning process thereby enhancing image segmentation accuracy and achieving satisfactory segmentation results. Finally BCE loss binary cross entropy loss and Dice loss functions were combined in the network model training process to accelerate the rapid convergence of the network and better optimize the model so as to improve the segmentation accuracy of the model. Results The experimental verification on the PASCAL VOC2012 dataset showed that the segmentation accuracy of the algorithm was increased by 2. 82% and the number of parameters was reduced by 14. 46 M. Furthermore experimental results on the Cityscapes dataset confirmed the superiority of the proposed algorithm. Conclusion The optimized DeeplabV3+ network model enhances the performance of the network model.