Existing point cloud semantic segmentation methods based on Transformer model typically rely on an encoder-decoder structure with same-level skip connections. However, this structure often leads to difficulties in effectively fusing encoder features and decoder features during the feature aggregation process, and results in the loss of details during sampling. To address these issues, this study proposes an improved version of the Point Transformer v2 model based on full-scale skip connections and channel attention mechanisms. First, full-scale skip connections are utilized to effectively fuse shallow and same-level encoder features with previous decoder features, providing the decoder with rich, multi-scale information. This enhances the model's ability to perceive complex scenes and results in finer segmentation. Then, channel attention mechanism is employed to adaptively adjust the model's attention to the features of each channel in the feature sequence, further improving segmentation accuracy. Finally, the simple linear layer in the improved group vector attention mechanism is used as the weight coding layer, which significantly reduces the number of parameters in the model. Experimental results show that the proposed model achieves excellent segmentation results on the S3DIS, with a mean intersection over union (mIoU) of 71.5%, a 1.9 percentage points improvement over Point Transformer v2. Meanwhile, the model's parameter count is reduced by 14.16% compared to Point Transformer v2. The experimental results demonstrate that the proposed model offers superior segmentation performance and higher parameter efficiency.