The determination of the current rice grade mostly relies on manual picking and weighing, the discrimination process has defects such as strong manual subjectivity, slow detection speed and low efficiency. Therefore, it is an inevitable trend in the rice industry to realize rapid and automatic determination of rice grades. Based on machine vision technology, this paper designs and develops a rapid automatic determination system for rice grades. This system obtains images of rice grains with high-resolution through imaging technology, uses Watershed algorithm and adaptive threshold function to process the images, marks different grains and uses convolutional neural network training, selects the optimal training model to classify brown rice, Use linear regression to analyze the data to realize the judgment of rice grade. It has been proved by experiments that the similarity between the system and the artificial judging results of the same batch of rice can reach 91. 4%. The system designed by this method not only eliminates the human subjectivity in the process of judging the rice grading, but also detects the speed that has been significantly improved, thereby improving the efficiency of rice grading and judgment.