With the policy of “carbon peaking and carbon neutrality” put forward in China, carbon emissions in the mining area have become the focus of attention. The study was based on soil samples taken from the mine areas, combined with 6 mathematical transformation methods (R, , Log(1/R), 1st, MSC, SNV) and spectral feature screening methods (CC-SPA). This study explored the hyperspectral response characteristics of soil carbon emissions under different land use types in Hongshaquan Open-pit Coal Mine in Xinjiang; combined with soil temperature (ST), Soil moisture (SM) and 6 kinds of spectral indexes (NDVI, RVI, NGLI, SMM, SI-T, ATI), using partial least squares (PLSR), support vector machine (SVM), random forest (RF), genetic optimization neural network (GA-BP) algorithm to obtain the optimal remote sensing of soil carbon emissions inversion model. The main conclusions are as follows: (1) The reflectance of soil in the non-mining affected area is higher than that in the mining affected area under natural conditions, and the southern line is the most affected by coal mining and has the lowest reflectance, which proves that mining activities have an impact on the mining area soil; (2) Spectral characteristics In terms of screening, the number of carbon emission characteristic bands extracted based on the correlation coefficient-continuous projection algorithm (CC-SPA) is much smaller than that of the correlation coefficient method (CC) and the continuous projection algorithm (SPA), and the characteristic bands present a certain clustered distribution. In the wavelength range of 1 600～2 200 nm, the number of characteristic bands during the day is much higher than at night. Compared with the daytime, the characteristic bands at night have the characteristics of obviously shifting to long waves. (3) Adding the spectral index based on reflectivity and the inversion model of ST and SM can significantly improve the accuracy of estimating soil carbon emission rate. The support vector machine (SVM) model based on the first-order differential transformation (1st) can invert the mining area. Comprehensive land use types have the best effect on soil carbon emissions (validation set R²=0.813, RMSE=0.116); the optimal combination of soil carbon emission indices for five different land use types is different, and the introduction of different spectral indices has a significant effect on soil carbon emission rates. The estimation accuracy has been improved to varying degrees (the verification set R² is above 0.8), and the optimal soil carbon emission inversion model can more accurately estimate the carbon emission rate of different land use types in the Hongshaquan mining area. This study can provide a basis for the remote sensing inversion of soil carbon emissions in desertified mining areas, quantitatively identify the carbon source-sink effect of soil under different land use types and realize the non-destructive detection of carbon emissions in mining areas, providing support for my country’s “30·60” double carbon goal. Data support.