Boletus is rich in nutrition, which is favored by consumers all over the world. Due to the differences of species and environmental factors, the quality of boletus of different species and origin vaires. At present, the shoddy, which undermines the sales of genuine boletus and the mushroom market, not only poses a health risks to consumers, but also restricts the international trade of boletus. In this study, the data fusion strategy was used to identify the species and origin of boletus, in order to provide a rapid and effective solution for tracing the source of edible fungi and correctly evaluating their quality. The test samples Boletus griseus, B. umbriniporus, B. edulis, Leccinum rugosicepes and B. tomentipes of five species of boletus fungi fruiting bodies collected from Baoshan, Kunming, Yuxi and Honghe Prefecture of Yunnan province. The chemical information was collected with Fourier transform infrared spectroscopy (FT-IR) and UV-Visible spectrophotometer (UV-Vis). The Kennard-Stone algorithm was used to divide the raw data of samples into calibration sets and validation sets. The calibration set established partial least squares discriminant analysis (PLS-DA) models based on FT-IR, UV-Vis, low-level, mid-level and high-level data fusion. The determination coefficients R2cal, predictive ability Q2, root mean square error of estimation (RMSEE) and root mean square error of estimation (RMSECV) were used to evaluate the robustness of the model. The results showed that: (1) The peak position, peak shape and number of peaks of FT-IR and UV-Vis absorption peaks of different species and origin were similar, and there were differences in absorption intensity. This showed that the chemical compositions of boletus were similar, but the content was different. (2) Two-dimensional scatter plots of PLS-DA model. It can be seen that mid-level fusion is better than low-level fusion to identify sample species and origin. (3) In each model, the mid-level fusion model has a larger Q2 and a minimum RMSECV, it showed that the model has the strongest robustness. (4) The test sets used to verify the model generalization ability, the correct rate of FT-IR, UV-Vis, low-level, mid-level and high-level data fusion model of samples kind identification were 92.86%, 35.71%, 97.62%, 100%, 95.23%, respectively; the correct rate of origin identification were 71.43%, 61.90%, 61.90%, 97.62%, 76.19%. The results showed that the data fusion is better than the independent model to some extent. Among them, the correct rate of mid-level data fusion is 100% in species identification, and the accuracy in origin identification is 97.62%. Mid-level data fusion model has better identification effect and generalization ability. FT-IR and UV-Vis combined with mid-level data fusion strategy can achieve the rapid and accurate identification of the boletus species, the fast and effective identification of origin. It can be used as a new method for traceability and quality evaluation of edible fungi.