Water surface pollutants of rivers are the main pollutants that endanger river resources. Timely detection and treatment of water surface pollutants can effectively protect the river environment and water resources and further boost the pollution and carbon reduction and the carbon sink capacity of the ecosystem. With the widespread promotion of intelligence, traditional monitoring and processing methods for water surface pollutants of rivers can no longer meet the current needs. To address water surface pollution in the Liaohe River basin, this study applies computer vision technology to the classification of water surface pollution and proposes a classification algorithm module based on grouped convolution and the dual attention (GCDA) mechanism for images of water surface pollution. Specifically, a simplified dual attention mechanism is introduced into the network on the basis of grouped convolution, which uses fewer parameters to enhance the network’s ability to extract features of images and further enhances the effect of image classification. The method of capturing images at a fixed position is performed on images from five river monitoring cameras in the Liaohe River basin for preprocessing. The five cameras refer to the ones in the hot spring intake of Chengshui Station, the confluence of Wangyinghe River and Xihe River, Gaotaizi Section, Jinyuan Sewage Outlet, and the overflow port of Qingyuan Sewage Treatment Plant. In addition, a dataset for water surface pollutants of rivers is established, and these images are categorized as polluted and unpolluted ones. Experiments indicate that compared with the original network and the network that adds space and channel attention mechanisms separately, the network with the GCDA module demonstrates better performance on this dataset in the dichotomous classification of images of water surface pollutants.