The excessively high content of heavy metals in the soil is hugely harmful, not only causing serious environmental pollution, but entering the human body through the food chain poses a serious threat to human health, so it is very important for heavy metal detection. X-ray fluorescence spectroscopy has been widely used because of its short detection time, non-destructive testing, and low testing costs. However, the detection of spectral data is severely disturbed by soil moisture factors, which leads to lower accuracy in estimating the heavy metal content in the soil directly. Taking the soil samples of Mancheng District, Baoding City, Hebei Province as the research object, the collected soil samples were cleaned, screened, dried, and then added with a certain amount of heavy metal solution to prepare samples with different water content and heavy metals for detection. The Mahalanobis distance and NJW clustering were calculated for the abnormal data in the experiment, and the influence of soil moisture content on the heavy metal spectrum was analyzed, the results show that the spectral repeatability of different water content is poor, and the spectral intensity decreases nonlinearly with the increase of soil water content. The Savitzky-Golay convolution smoothing denoising method and linear background method are used to preprocess the spectrum to solve the problems of noise and baseline drift caused by the environment and the instrument itself. A non-negative matrix factorization algorithm was used to deal with the peak signal-to-noise ratio evaluation model to determine the number of end elements. The results show that the peak signal-to-noise ratio tends to increase when the number of end elements increases to 10. The stable fluctuation is very small. After the non-negative matrix decomposition treatment, the spectrum repeatability and similarity are good among the same heavy metal content and different water content. The correlation coefficient between the spectra is calculated to prove the similarity between the spectra further. A partial least squares prediction model was established after removing the water content for spectral interference. In order to verify the accuracy of the prediction model, a partial least squares prediction model with no water content removed was established, and the partial water content was removed by orthogonalization with external parameters The least squares prediction model is evaluated using the evaluation parameter determination coefficient (R²), cross-validated root mean square error (RMSECV), average absolute error (MAE), and relative analysis error (RPD). Validation results show that compared to models built without removing water content, non-negative moments are used partial least squares model established by matrix decomposition and removal of water content R² and RPD increased by 0.019 7 and 1.029 2, RMSECV and MAE decreased by 2.386 3 and 1.439 6; Compared to the partial least squares model established by the external parameter orthogonalization method, the RPD and RPD increased by 0.009 9 and 0.108 1, and the RMSECV and MAE decreased by 0.244 7 and 0.356 6, it is shown that the model established after denoising by non-negative matrix decomposition can effectively improve the accuracy and robustness of prediction. Non-negative matrix factorization can effectively eliminate the effect of soil water content on the spectrum, and the partial least squares model established on this basis has realized the inversion of soil heavy gold content and provided certain technical support for quantitative detection of heavy metals.