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Large amount of grain phenotypic information is needed in researches such as digital grain traits investigation, phenotype and gene association analysis and digital agriculture simulation. A method for automatic extraction of grain high-throughput phenotypic information based on point cloud was proposed, aiming to automatically obtain three-dimensional (3D) grain model and 40 phenotypic parameters. Firstly, the classification of grain point cloud was completed through cluster analysis. Secondly, 3D grain model was reconstructed with cylindrical mesh method. Finally, according to the characteristics of different phenotypic parameters, 11 primary parameters, 11 derived parameters and 18 shape factors were automatically extracted. Experiment using data obtained by hand-held laser scanner (Handyscan 700) showed that the measurement result could reach millimeter level. The weight of each phenotypic parameter was analyzed based on principal component analysis method. With parameters measured by vernier caliper and Geomagic Studio as the true value, the average relative error of length, width, height, surface area and volume, the cross-sectional area of three principal component sections was 1.14%, 1.15%, 1.62%, 0, 1.82%, 2.12% and 2.43%, respectively. Compared with the manual measurement method and the software measurement method, the results of the proposed method was competitively accurate, which had advantages of batch processing, automation, less manual intervention (only in data acquisition) and high efficiency.