Mid-infrared (MIR) and near-infrared (NIR) spectroscopy combined with chemometrics were explored to classify Cabernet Sauvignon wines from different countries (Australia, Chile and China). Commercial wines (n = 540) were scanned in transmission mode using MIR and NIR, and their characteristic fingerprint bands were extracted at 1750-1000 cm-1 and 4555-4353 cm-1. Through the identification system of Tri-step infrared spectroscopy, the correlation between macroscopic chemical fingerprints and geographical regions was explored more deeply. Furthermore, Principal component analysis (PCA), soft independent modelling of class analogy (SIMCA) and discriminant analysis (DA) based on MIR and NIR spectra were used to visualize or discriminate differences between samples and to realize geographical origin traceability of Cabernet Sauvignon wines. Through "external test set (n = 157)" validation, SIMCA models correctly classified 97%, 97% and 92% of Australian, Chilean and Chinese Cabernet Sauvignon wines, while the DA models correctly classified 86%, 85% and 77%, respectively. Based on unique digital fingerprints of spectroscopy (FT-MIR and FT-NIR) associated with chemometrics, geographical origin traceability was achieved in a more comprehensive, effective and rapid manner. The developed database models based on IR fingerprint spectroscopy with chemometrics could provide scientific basis and reference for geographical origin traceability of Cabernet Sauvignon wines (Australia, Chile and China).