Abstract: Based on hyperspectral data pretreatment and hyperspectral spectral bands extraction of soil organic matter, remote sensing estimation models of multiple linear regression, multilayer perception, k-nearest neighbor, bagging and random forest were built.Cross-validation method was applied to evaluate the accuracy of 5 models with the help of 5 indexes:correlation coefficient establishment, mean absolute error, root mean squared error, relative absolute error, and root relative squared error.The most accurate model was selected for remote sensing and spatial analysis of soil organic matter in wetlands.Research results showed that the sensitive hyper-spectral band wavelength to soil organic matter was mainly concentrated in 925 nm, 1 144 nm, 1 477 nm, and 1 780 nm 4 bands.Among 5 prediction models, the performance of the multiple linear regression was the best, followed by random forest.The spatial distribution of soil organic matter presents a zonal distribution pattern gradually increasing from the middle of the bank to the periphery.The soil organic matter content of Xinjizhou wetland was the highest, 2.22%, followed by the forestry near wetland.The soil organic matter of the agricultural land and bare land with the lowest vegetation coverage was the lowest, 0.43%.The spatial distribution pattern of soil organic matter is closely related to the zonal distribution of soil types in the study area.