An automated bolt measurement method based on machine vision is proposed to address the limitations of low efficiency and accuracy in bolt size measurement and the reliance on manual operations in the aerospace manufacturing industry. First, the acquired bolt images are preprocessed, and subpixel contour coordinate vectors of the bolts are extracted. Subsequently, a binary tree structure is established based on the contour vectors, where a pre-order traversal of the contour binary tree is used to stitch the contours, achieving the initial decomposition of the bolt contour. The decomposition results are further optimized in the curvature scale space. Finally, a three-level feature tree is developed based on the relationship between the decomposed contour segments and the bolt features to be measured, enabling multi-feature recognition and size measurement of bolts. Experimental validation shows that the proposed algorithm achieves a recall rate of 98% for bolt feature recognition, an average absolute error in size measurement within 0.010 mm, a repeatability error within 0.005 mm, and an average measurement time of only 1.41 s. The results indicate that the proposed algorithm can accurately recognize multiple features of different bolt types, offer highly adaptive measurement capabilities, and fulfill the high-precision automated inspection requirements for aerospace bolts.