Large-scale point cloud semantic segmentation is crucial for understanding the scene environment in a variety of applications such as autonomous driving. To address the problem that existing large-scale semantic segmentation methods cannot fully exploit the local neighborhood information of point clouds and are prone point features, we propose a local parallel feature extraction (LPFE) module that enhances the feature representation of points by separately coding the semantic information and geometric structure of each point in point clouds. The LPFE module consists of three blocks, including the semantic information encoding (SIE) block, the position information encoding (PIE) block, and the parallel mixed pooling aggregation (PMPA) block. (1) The SIE block uses the proposed adaptive feature fusion strategy to generate rich and higher-level semantic features for each point. (2) The PIE block enables the network to extract fine-grained geometric features while focusing on the entire local region by establishing relative and global position relationships in the neighborhood. (3) The PMPA block uses a bilateral structure to capture both the maximum local features and local contextual features using mixed pooling operations. The above blocks form a new network for semantic segmentation of large-scale point clouds, called “local parallel feature extraction network,” which achieves competitive performance for semantic segmentation on large-scale point cloud datasets S3DIS and Semantic3D, and demonstrates the effectiveness of the modules through specific ablation experiments.