AI model closed-loop iterative training method and system based on active learning training sample

The invention discloses an AI model closed-loop iterative training method and system based on an active learning training sample. Selecting part of pictures from the sample picture set to mark to obtain a mark data set, and dividing the mark data set into a training data set and a test data set; training the deep learning model by using the labeled data set to obtain an initial training model; selecting part of picture labels from the sample picture set by using the initial training model and combining the part of picture labels into a training data set; updating the initial training model by using the updated training data set and the test data set; and repeatedly executing the operations of updating the training data set and updating the initial training model until the scale of the training data set reaches a preset number or the model performance meets the requirement. According to the method, part of samples are selected for labeling through active learning, time and resource waste caused by labeling of