External tagging methods facilitate the study of the spatio-temporal behavior of Apostichopus japonicus at population and individual levels, as well as population dynamics, breeding, and efficient harvesting methods. However, due to the soft body wall and strong exclusion ability of Apostichopus japonicus, the retention rate of traditional invasive tagging methods is low. Moreover, damage caused to the body wall by traditional tagging methods often leads to wound ulceration, which affects the quality of life of Apostichopus japonicus. In this study, a non-invasive identification method for Apostichopus japonicus is developed by processing images using a deep convolutional neural network model to extract features and characterize the unique body texture patterns of individual specimens. After feature extraction and the training of the classifier on consecutive images of Apostichopus japonicus taken in a period of 50 d, the classifier achieved a maximum accuracy of 0.996±0.011 on the test set compared to traditional invasive tagging methods that only achieved an accuracy of up to 0.75. For individual temporal tracking recognition, feature extraction and model training were performed using images taken in a period of 1 to 25 d. The classifier achieved an accuracy of 0.946±0.058 on the test set consisting of images taken in the periods of 26–50 d. These results indicate that the use of the ResNet50 convolutional neural network can effectively predict the categories of Apostichopus japonicus and achieve a better accuracy than traditional tagging methods in the temporal tracking task.