The current point cloud registration methods cannot effectively address resolution mismatches， partial overlaps of point clouds， and numerous noise points when used for cultural relic models such as Terra-cotta Warriors. Hence， a ResUNet registration model based on the dynamic graph attention mechanism is proposed. The model integrates the residual module into the U-Net， performs three-dimensional （3D） sparse voxel convolution to calculate the features of point clouds， and applies a new normalization technology known as batch-neighborhood normalization to improve the robustness of features against point density changes. To improve the registration performance， the model aggregates local and context features via self- and cross-attention mechanisms. Finally， a random sampling consensus algorithm is used to estimate the change matrix between the source and target point clouds to complete the robust registration of the Terra-cotta Warriors model. To verify the effectiveness and robustness of the proposed method， four datasets （3DMatch， 3DLoMatch， 3DMatch with resolution mismatches， and two sets of terra-cotta warrior data） were used to test the registration model. Experimental results show that the registration recall was 90.1% and 61.0% in the 3DMatch and 3DLoMatch datasets， respectively. In the mismatched-resolution 3DMatch dataset， compared with feature learning-based registration algorithms， our algorithm improved the registration recall by 5%–20%. In the terra-cotta warrior dataset， the relative rotation and translation errors were less than 0.071 and 0.016， respectively， which are several times to one order of magnitude lower than those of other algorithms. The model proposed herein can extract key feature information from a 3D point cloud and is more robust to variations in point density and overlapping compared with other models.