Objective: To achieve reasonable and effective grading of live crayfish, and improve the work of grading crayfish. Methods: The construction of crayfish image shooting platform, to obtain the original image of crayfish, and the semantic segmentation dataset which segmented the three parts of the crayfish head, crayfish pincers, and crayfish tail was created. The correlation between the actual weight of three parts and the corresponding pixel size in the dataset was analyzed, and a new grading standard for crayfish which was according to the proportion of head and pincers in the whole crayfish was summarized. The DeepLab V3+ neural network was trained using the crayfish semantic segmentation dataset, and the test set was used to test the semantic segmentation effect of the model and the accuracy of crayfish grading. Semantic segmentation evaluation criteria were mean intersection over union （MIoU）, mean pixel accuracy （MPA） and pixel accuracy （PA）. Results: The MIoU of the crayfish semantic segmentation test set was 94.35%, the MPA was 96.56%, and the PA was 99.44%. The accuracy of crayfish grading in the test set was 85.56%. Conclusion: The DeepLab V3+ model can accurately segment crayfish images and estimate the proportion of crayfish head and pincers, and the model can complete the crayfish grading task.