China is the world’s largest exporter of Boletus, and Yunnan Province is the largest producer of Boletus eduils in China. The delicious Boletus eduils is fragrant and nutritious, and it is popular among consumers. However, due to different geographical climates and environmental differences, the quality is uneven. The production of Boletus edulis in different production areas in Yunnan Province was identified and the quality control was improved. In this study, 124 samples of delicious Boletus edulis from 13 producing areas around Yunnan were collected, using Fourier to transformed mid-infrared spectroscopy (FTIR-MIR), Fourier transform near-infrared spectroscopy (FTIR-NIR), and UV-visible spectroscopy (UV-Vis). Inductively coupled plasma atomic emission spectrometry was used to determine the spectral information and mineral content of different parts and analyze them. The original spectrum is smoothed (Savitzky-Golay SG), second derivative SD, standard normal variate (SNV) and other pre-processing. The data is divided into a training set and prediction set by Kennard-Stone classification. The classification model is established by partial least square discriminant analysis (PLS-DA) and support vector machine (SVM), and then the comparative analysis is carried out to find the best method of origin identification. The results show that: (1) The recovery rate of standard tea materials in the element determination method is between 91.00% and 106.00%, and the method is accurate and reliable. (2) Boletus edulis is rich in elements K, P, Mg, Na, Ca and the same place of origin in different parts. There are differences between the same parts in different places, which may be different from the enrichment ability of different parts of Boletus eduils. The geographical environment of the place of origin is related. (3) Intermediate fusion extracts important information through Principal component analysis (PCA). The cumulative contribution rate of FTIR-MIR and UV-Vis spectral data reaches 83.50% and 66.70%, which can represent important information variables. (4) In the PLS-DA and SVM models, the identification effect of the data after fusion is higher than that of the single data identification, indicating that the data fusion strategy is effective in the identification of delicious bovine liver. (5) Using the Hoteling T2 to perform the outlier tests on data fusion. The results show that the model establishment does not exceed the confidence interval, and the model has accuracy and credibility. (6) The primary fusion and intermediate fusion results of the PLS-DA model are higher than the SVM, indicating that the PLS-DA models intermediate fusion can be used as the best method for identification. Multi-spectral combined with mineral elements in different parts can accurately identify the delicious Boletus eduils from different habitats, and provide an effective analysis method for the regional quality difference evaluation of Yunnan Boletus eduils.