To address the problems of disorder,uneven sampling,and the poor segmentation of workpiece point clouds with mutual occlusion,a multiscale adaptive channel attention point cloud segmentation network (PointECA) was proposed.In this algorithm,multiscale feature extraction module was used to better fuse the local neighborhood features of different scales and richer global feature information was obtained; the adaptive channel attention module was used to interactively learn the channel dimensions of local features at different scales to achieve a better semantic segmentation effect.In addition,the Workpieces dataset for semantic segmentation experiments was produced.A large amount of experimental data shows that PointECA achieves 95.42% mean intersection over union for workpiece part segmentation in disordered and mutually occluded scenes,which can provide better conditions for the fast sorting disordered workpieces.