Abstract: Objectives: Existing deep learning-based 3D point cloud semantic segmentation methods often overlook global contextual information and do not fully leverage the synergy between the local geometric structure of the point cloud, color information, and high-level semantic features. To effectively capture the geometric structure, color variations, and semantic features of point clouds while retaining global context information. Methods: We propose a point cloud semantic segmentation model that integrates local feature encoding and dense connectivity. First, a local feature extraction module is employed to enable the model to concurrently capture spatial geometric structure, color information, and semantic features. Second, a local feature aggregation module is incorporated to preserve the rich geometric data within the original point cloud, minimizing information loss during feature extraction. Finally, we utilize a dense connectivity module to aggregate contextual semantic information, promoting synergy between low-level features and high-level semantic data. Results: Our model is benchmarked against two large datasets, S3DIS and Semantic3D. The results show that our proposed network model achieves an Overall Accuracy (OA) and mean Intersection over Union (mIoU) of 88.3% and 71.8% on the S3DIS dataset, improving the baseline set by RandLA-Net by 0.3% and 1.8% respectively. On the Semantic3D dataset, we registered an OA of 94.9% and an mIoU of 77.8%, marking respective improvements of 0.1% and 0.4% over RandLA-Net. Conclusions:Our model effectively preserves local geometric and color information through local feature encoding. The Local Feature Aggregation Module refines point proximity along boundaries to align with similar feature domains. Dense connections successfully integrate global context and key geometric features. Overall, our approach delivers more accurate semantic labels and a superior geometric feature representation, enhancing the precision of local segmentations.