Acquiring accurate anatomy information of intracranial artery from 3D digital subtraction angiography (3D-DSA) is crucial for intracranial artery intervention surgery. However, this task often comes with challenges of large-scale image and memory constraints. In this paper, an effective two-stage framework is proposed for fully automatic morphological analysis of intracranial artery. In the first stage, the proposed Region-Global Fusion Network (RGFNet) achieves accurate and continuous segmentation of intracranial artery. In the second stage, the 3D morphological analysis algorithm obtains the access diameter, the minimum inner diameter and the minimum radius of curvature of intracranial artery. RGFNet achieves state-of-the-art performance (93.36% in Dice, 87.83% in mIoU and 15.64 in HD95) in the 3D-DSA intracranial artery segmentation dataset, and the proposed morphological analysis algorithm also shows effectiveness in obtaining accurate anatomy information. The proposed framework is not only helpful for surgeons to plan the procedures of interventional surgery but also promising to be integrated to robotic navigation systems, enabling robotic-assisted surgery.