Efficient and accurate extraction of spatial structure information is crucial in point cloud semantic segmentation to understand three-dimensional scenes. To address the unstructured nature of point cloud data, we propose a point cloud segmentation method, PCANet, that effectively integrates position encoding and channel mechanisms to reduce redundant relationship learning and computational costs. PCANet first applies position encoding technology to capture the relative positional information of the point cloud data. Next, the encoded feature maps are weighted using a channel attention mechanism, which amplifies the representation ability of key features across different channels and expands the network's receptive field. The experimental results demonstrat that PCANet achieves strong segmentation performance on both the ShapeNet and S3DIS point cloud datasets. For ShapeNet, the instance mean intersection-over-union ratio (mIoU) reaches 87.4%, and the category mIoU reaches 85.8%, showing improvements of 2.3 percentage points and 3.9 percentage points, respectively, over PointNet++. In addition, the mIoU on the S3DIS dataset reaches 73.7%, outperforming PointNet++ by 20.2 percentage points. These results demonstrate that the proposed method performs well in segmenting small components and indoor point cloud scenes.