Accurately obtaining information on the number, location, and spatial distribution of check dams is fundamental for scientifically analyzing their erosion reduction and sediment trapping effects, as well as for future construction planning. This study focuses on the Yanhe River Basin in the Loess Plateau, utilizing high-resolution imagery from GF-2 and Google Earth as data sources. Deep learning object detection algorithms (Faster R-CNN, YOLO v3, Cascade R-CNN, and YOLOX) and semantic segmentation algorithms (FCN, U-Net, PSPNet, and DeepLab v3+) were employed to identify and extract the check dam bodies and dam lands. Geographic analysis methods and comprehensive interpretation were used to optimize the extraction results of the check dams. The results are as follows: (1) In the identification of check dam bodies, the YOLOX model performed the best, achieving an average precision (AP) of 69.4% at an IoU threshold of 0.50:0.95. Using this model to identify images of the Yanhe River Basin, 1440 detection boxes were obtained, with an accuracy of 75.8% and a recall rate of 85.7%. (2) In the extraction of dam lands, all four models performed well, with an average IoU, precision, recall, and F1 score all exceeding 85%, 90%, 85%, and 88%, respectively. The majority voting method was used to combine the predictions of the four models, resulting in the extraction of 92.0 km2 of dam lands in the production and operation period and 10.6 km2 in the water storage and sediment trapping period. (3) By incorporating geographic analysis methods, the accuracy of check dam body identification increased from 75.8% to 84.5%, and the precision of dam land extraction improved from 90.6% to 96.7%, with the efficiency of identification and extraction doubling. These results indicate that the proposed method can be rapidly and accurately applied to the identification of check dams in the Loess Plateau, which is crucial for analyzing their impact on the ecological environment and for comprehensive management of small watersheds.