Large-scale point cloud semantic segmentation is crucial for various real-world applications such as autonomous driving and remote sensing. Existing methods either fail to explore large-range contextual information, require preprocessing steps, or cannot adequately exploit point cloud features. To address these challenges, we propose TriEn-Net, an efficient framework for large-scale point cloud semantic segmentation. TriEn-Net directly inputs the whole point clouds to capture large-range contextual information and distinguishes the point cloud representations as points and high-dimensional features. By developing the complementary Non-parametric Encoding Module and Non-parametric Attention Module to encode spatial coordinates and semantic features respectively, TriEn-Net captures rich information and enhances feature representation capability without introducing additional parameters. Comprehensive experiments on S3DIS and SemanticKITTI datasets demonstrate TriEn-Net’s superior performance even with fewer parameters, making it a promising solution for efficient and effective semantic segmentation on large-scale point clouds.