Satellite remote sensing technology was used to obtain high-resolution remote sensing image water body data set. Based on deep convolutional neural network, water body was extracted from high-resolution remote sensing images and intelligent monitoring of impaired water body was carried out. An Improved Water Detection Network (IWDNet) model based on the proposed U-Net was proposed. Firstly, based on the U-Net structure, skip multi-scale feature fusion was introduced, and different Multi-scale Feature Fusion Attention Mechanisms (MFFAM) modules were generated by combining SE, ECA and CBAM attention mechanisms for comparison. The dilated convolution was introduced to expand the network receptive field. Finally the recognition and detection of impaired water bodies were realized. Experiment results show that the MFFCBAM-IWNet model based on skip multi-scale fusion and CBAM attention mechanism effectively improves the recognition accuracy, and performs best on the high-resolution remote sensing image water body data set. The overall accuracy is 98.56%, and the Kappa coefficient is 0.9784.