Intracranial aneurysms refer to the pathological expansion of blood vessels in the brain. Once an aneurysm ruptures, the fatality rate is extremely high. Manual detection of intracranial aneurysms is cumbersome and time-consuming, making the introduction of automated detection technology necessary. To address the insufficient space utilization of three-dimensional (3D) point cloud data by existing algorithms and the difficulty in fully capturing both local details and overall structural information, a segmentation algorithm based on patch serialization attention for intracranial aneurysm point clouds is designed. Space-filling curves are used to serialize point clouds, which improves the effectiveness of traditional point clouds in extracting relationships between different local structures. A more refined patch attention mechanism is introduced, with patch grouping and patch interaction, to strengthen the model’s ability to generalize spatial relationships. The issues related to uncontrollable information density and overlap in traditional pooling methods are resolved through graph pooling operations. On the IntrA dataset, the segmentation accuracy achieved is 95.37% IoU for healthy blood vessels and 84.67% IoU for aneurysm segments, significantly outperforming most existing algorithms.