To address semantic confusion in point cloud semantic segmentation algorithms when processing fine details of nursery plants, this paper introduces NurSegNet, a specialized point cloud semantic segmentation network designed for nursery environments. To overcome the challenges of low inter-class variability and high semantic category similarity, the model incorporates a multi-scale feature weighted fusion module that integrates characteristics at various scales to enhance detail recognition capabilities. For comprehensive neighborhood feature extraction, a multi-head self-attention local feature aggregation module implements multi-head self-attention in neighborhood spaces, enriching local feature semantic information. The model also incorporates horizontal distance encoding to expand local feature spatial information. Additionally, a maximum probability sampling method is implemented, which substantially improves sampling efficiency compared to farthest point sampling while maintaining downsampling coverage. Semantic segmentation experiments conducted on a custom nursery dataset demonstrate that NurSegNet achieves a mean intersection over union (mIoU) of 87.89%, an overall accuracy of 97.42%, and a mean accuracy of 94.27%, surpassing conventional point cloud segmentation networks such as PointNet++ and RandLA-Net. This approach effectively addresses semantic confusion and fulfills the requirements for high-precision nursery semantic map construction.