Abstract. Accurate and effective extraction of water body information is an important prerequisite for hydrological studies of the Yellow River. However, there is a scattered and frequently swing water flow in the middle and lower reaches of the Yellow River. Traditional water body extraction methods mainly rely on handcrafted statistical features, which cannot fully extract river body in real-world conditions. To deal with these problems and achieve more accurate results, an AU-Net network is proposed to expand the receptive field of the convolutional kernel and incorporate the detailed information of multi-scale features, which improves the ability to extract the middle and lower reaches of the Yellow River from remote sensing images. The experimental results illustrate that compared to the other methods, the AU-NET model has higher recognition accuracy (MPA = 0.97 and MIoU = 0.99) on the water body dataset in the middle and lower reaches of the Yellow River. And the network has high robustness and good fitting, which can better extract the middle and lower reaches of the Yellow River.