Eucommia ulmoides is one of valuable cash crops and its leaves are a high-quality raw industrial material with great development potential. Geographical variation is the main factor leading to differences in chemical composition of Eucommia ulmoides leaves (EULs). In this study, a total of 159 samples from 13 provinces in China including male and female individuals as well as various elevation ranges were systematically conducted using fusion data, attenuated total reflection Fourier transformation mid-infrared (ATR-FTIR) and ultraviolet-visible (UV–vis) spectra, coupled to chemometrics. Two classification models, partial least squares discrimination analysis (PLS-DA) and support vector machine (SVM), were established based on individual spectra and multi-spectral fused information, respectively. Comparatively, the SVM model based on genetic algorithm (GA) searching for optimal parameters had the best performance for distinguishing different origin samples with 100 % accuracy rates in calibration and validation sets. Furthermore, hierarchical cluster analysis (HCA) was used for investigating the influence caused by the difference in gender and altitude based on low-level fusion data. The result showed that the effect of individual gender on chemical information of EULs was less than the influence of geographical factors. Meanwhile, an interesting focus was that the PLS-DA scores plot and dendrogram suggested that the chemical profiles of these samples in Jiangxi Province (region 8) was significantly different from other regions because of the green circular economy mode. This study indicated that the PLS-DA and GA-SVM algorithm could be developed as an excellent model in geographical traceability on the basis of mid-level (latent variables, LVs) data fusion with two spectral datasets. Such comprehensive utilization model under the circular industry economy should be recommended.