Similar to how humans perceive 3D objects, neural networks discern the class labels of point clouds by combining local and global features of the structures and performance. Based on this, we reviewed the pipeline of few-shot point cloud semantic segmentation and identified three issues: the neglect of local information by the neural network, the lack of receptive field for point cloud features and the domain gap between the support and query set data. To address them, we propose a novel network called AttenPoint. It incorporates three attention-based modules, the attention pooling is used to extract local feature accurately, the attention feature enhancement aims to broaden the global feature receptive field and the attention segmentation head aims to achieve transfer across domains with limited samples. Experimental results on the S3DIS and ScanNet datasets demonstrate that AttenPoint has achieved state-of-the-art(SOTA) performance in few-shot semantic point cloud segmentation task.