Three-dimensional （3D） part assembly is an important task in 3D computer vision. It aims to estimate the poses of a set of 3D parts and accurately combine them into a target structure. However， existing methods mainly rely on large-scale data for training and learn from past experience to complete assembly， resulting in weak generalization and poor adaptability to new assembly tasks. To address the problem of insufficient generalization in 3D part assembly， assembly instructions with line drawings were introduced as auxiliary information， with the expectation that robots could establish correspondence between 3D parts and regions in 2D line drawings. Nevertheless， establishing such a correspondence faced many challenges. Firstly， multiple identical 3D parts often existed in the assembly， but their corresponding 2D regions had different shapes and positions， which posed difficulties for neural networks in establishing such 3D-2D correspondence. Secondly， occlusions among parts in the line drawings further complicated the establishment of these correspondences. Therefore， a 3D part assembly method based on line-drawing segmentation was proposed， consisting of two main stages. In the first stage， point cloud information was used to perform part instance segmentation on the line drawings， effectively establishing the 3D-2D correspondence of the parts； in the second stage， a graph convolutional network was used to integrate the image information with the segmentation results for component pose estimation， thereby completing the assembly task. On the PartNet dataset， the proposed method was compared with three baseline methods： single-stage， layer-by-layer assembly， and two-stage approaches， demonstrating that it consistently improves component assembly accuracy and validating its effectiveness.