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The body size parameters of live pigs are important criterion for evaluating the growth state of pigs. The manual measurement of body size is time-consuming and labor-intensive and easy to cause the stress response of pigs. The non-contact pig body size parameter measurement method was studied, referencing the manual measurement experience method, and the pig body size measurement method was proposed based on point cloud semantic segmentation. A non-contact pig point cloud collection platform was established to collect bilateral point cloud data of 3510 groups of pigs. The background point cloud was removed by the pass-through filter and random sampling consistent segmentation method. The outliers were removed by statistical filter. The point cloud was sparsed by voxel downsampling method to complete the pretreatment of pig point cloud. Based on PointNet network and combined with attention module the semantic segmentation model was constructed. The measurement method of pig body size was designed for different parts of segmentation. The experimental results showed that the accuracy of the improved semantic segmentation model was 86.3%, which was higher than that of PointNet, PointNet++ and 3D-RCNN. The maximum absolute error between the measured value and true value was 6.8cm, and the average absolute error was within 5cm, which had a high estimation accuracy. The method can be used for the measurement of pig body size. The research combined semantic segmentation with body size measurement, which can provide an idea for the non-contact measurement.