To address the insufficient local feature extraction and lack of context feature fusion in three-dimensional laser point clouds, we propose MAKNet, a point cloud feature extraction network that integrates a self-attention mechanism with a multi-level feature extraction architecture. Taking three-dimensional laser point cloud data as input, MAKNet employs an SAA module to extract point cloud features. It enhances sparse point recognition by introducing attention weights between the central point features and neighboring point features. Furthermore, MAKNet utilizes a multi-scale feature extraction approach to extract and fuse multi-layer point features, followed by point cloud feature skip connections to increase the coverage of the extracted point information. Experimental results demonstrated that on the S3DIS dataset, the overall accuracy of MAKNet was 86.9%, which was an improvement compared to that of PointNet++ (80.1%). On a self-built dataset of transmission line corridors, MAKNet achieved an overall accuracy of 96.4%, showcasing its robustness and strong generalization capabilities in semantic segmentation tasks.