Fish spawning grounds are affected by changes in environmental conditions. Rapid and effective identification of fish spawning grounds is crucial for assessing aquatic resources and protecting rare aquatic animals. In this study, based on aerial images captured by drones and field surveys, we identified 77 spawning grounds of Qinghai Lake naked carp (Gymnocypris przewalskii). By constructing a sampling method suitable to rivers and improving sampling technology, we obtained 807 sets of training samples, 56 sets of verification samples, and 23 sets of test samples; of these, the test samples were not enhanced. By linking the lightweight UNet and NestedUNet models in a chain, a UNet-NestedUNet deep learning model was established, and its performance was compared with UNet and NestedUNet models with twice the number of channels. The results showed that UNet-NestedUNet model performed better on the validation set. Model validation accuracy was the highest in the 51st step of training. The training intersections over union ratio and accuracy of the model were 0.870 and 0.996, respectively, and the validation intersection over union and accuracy were 0.648 and 0.985, respectively. Considering the test results, accuracy was lower than general remote sensing image or image segmentation accuracy. However, the deep learning model effectively identified most of the spawning grounds of Qinghai Lake naked carp (79%). In the entire area, the model identified 61 out of 77 spawning grounds, and only one spawning ground in the test area was not identified. In addition, the model identified a large number of unmarked areas as spawning grounds; these may be small areas not considered during manual identification, or misjudged by the model. This discrepancy may be caused by the relatively small number of actual samples used for training and the uncertainty of the spawning ground itself. The former can be resolved by obtaining more spawning ground samples of Qinghai Lake naked carp through long-term data accumulation. For the latter, it is necessary to further refine spawning field boundaries. At present, deep learning can be used as an auxiliary means to identify spawning grounds of Qinghai Lake naked carp. With the continuous increase in the number of cumulative samples in the future, the performance of the model will further improve. Therefore, the deep learning model has prospective applications in the identification of fish spawning grounds