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Aiming to address the issues of heavy manual workload and the potential for inducing stress responses in dairy cow during traditional body measurement, a three-dimensional (3D) reconstruction and point cloud segmentation approach was proposed. This approach utilized an improved point cloud segmentation model for the automatic calculation of body measurements in cow. The research focused on Chinese Huaxi cow, and 212 sets of point cloud data from 115 dairy cows were collected using a 3D point cloud acquisition system. The Super-4pcs algorithm was used for point cloud registration, followed by spatial pass-through filtering and neighborhood-based outlier filtering to complete the 3D reconstruction of the cow’s point cloud. The PointNet++ point cloud segmentation algorithm, combined with the spatial grouping enhancement (SGE) module, was used to propose the improved SGPointNet++ model for point cloud segmentation. The segmentation results were then used to measure four body parameters: height, chest girth, abdominal girth, and withers height. The experimental results showed that the mean intersection over union (MIoU) for segmentation using the SGPointNet++ model on the test set was 81.87%, which was 27.82 percentage points, 1.55 percentage points, 1.19 percentage points, and 1.07 percentage points higher than that of PointNet, ASSANet, PointNeXt, and PointNet++, respectively. The average absolute percentage errors for body measurements were 2.38%, 3.05%, 1.32%, and 1.69% for body height, chest girth, abdominal girth, and withers height, respectively. These results indicated that this method can be used for dairy cow body measurement, reducing workload while ensuring computational accuracy. It provided a methodological foundation for continuous animal phenotype measurement and offered technical insights for further improvements in segmentation and body measurement calculation models.