Aroma profiles of ginseng samples at different ages were investigated using electronic nose (E-nose) and GC–MS techniques combined with chemometrics analysis. The bioactive ginsenoside and volatile oil content increased with age. E-nose performed well in the qualitative analyses. Both Principal Component Analysis (PCA) and Discriminant Functions Analysis (DFA) performed well when used to analyze ginseng samples, with the first two principal components (PCs) explaining 85.51% and the first two factors explaining 95.51% of the variations. Hierarchical Cluster Analysis (HCA) successfully clustered the different types of ginsengs into four groups. A total of 91 volatile constituents were identified. 50 of them were calculated and compared using GC–MS. The main fragrance ingredients were terpenes and alcohols, followed by aromatics and ester. The changes in terpenes, alcohols, aromatics, esters, and acids during the growth year once again confirmed the dominant role of terpenes. The Partial Least Squares (PLS) loading plot of gas sensors and aroma ingredients indicated that particular sensors were closely related to terpenes. The scores plot indicated that terpenes and its corresponding sensors contributed the most in grouping. As regards to quantitative analyze, 7 constituent of terpenes could be accurately explained and predicted by using gas sensors in PLS models. In predicting ginseng age using Back Propagation–Artificial Neural Networks (BP-ANN), E-nose data was found to predict more accurately than GC–MS data. E-nose measurement may be a potential method for determining ginseng age. The combination of GC–MS can help explain the hidden correlation between sensors and fragrance ingredients from two different viewpoints.