In recent years, with the aggravation of soil heavy metal pollution and the gradual improvement of people's environmental awareness, the research on the rapid detection method of soil heavy metal content has been strengthened rapidly. At present, X-ray Fluorescence analysis (XRF) has been widely used to detect heavy metal pollution in soil. However, due to the limited energy resolution of the X-ray fluorescence spectrometer and the low fluorescence yield of some heavy metal elements, overlapping phenomena occurred in adjacent spectral peaks of some elements. In the cause of overlapping phenomenon often appears between adjacent peaks in X-ray Fluorescence analysis (XRF), a new overlapping peak analysis method based on Sparrow Search Algorithm (SSA) was proposed. Firstly, samples with different moisture content and heavy metal element content were prepared, and original spectral data were obtained by X-ray fluorescence spectrometer from the soil sampled of Baoding, Hebei. Then, the spectral data were preprocessed, the spectral clustering algorithm removed the abnormal spectral samples, the spectral denoising and background subtraction were completed by the Savitzky-Golay five-point quadratic denoising method and the linear background method. The random number method is used to generate a large number of simulated spectral data for the use of subsequent algorithms. After that, expectation-maximization (EM) was applied to analyze overlapping peaks preliminarily. Set the initial parameters of the EM algorithm, and put simulation spectra data into the EM algorithm. When it reached the maximum number of iterations, can preliminarily get parameters of the Gaussian Mixture Model (GMM), expectation, variance and weights of each Gaussian peaks. However, the EM algorithm is easily affected by the initial parameter and is prone to fall into the local optimum, leading to inaccurate results. Therefore, further optimization of the EM algorithm is needed. In this study, SSA was used for global optimization of parameters of the GMM. After setting the basic SSA algorithm parameters, 100 groups of parameters obtained by the EM algorithm were taken as the initial population of the algorithm, and then set appropriate fitness function. Finally, the optimal global parameters were obtained through iteration, and the decomposition of overlapping peaks was realized. Sparrow Search algorithm (SSA) is less affected by parameter setting. Compared with some traditional optimization algorithms, such as GA, ACO, PSO, etc. SSA has fast convergence speed and is not easy to fall into local optimal. Therefore, this algorithm can achieve better optimization results. The analysis of overlapping peaks shows that the algorithm can get more accurate results with fewer iterations and be widely used in energy spectrum overlapping peaks analysis.