To realize the rapid identification and analysis of aged wheat with different storage years, this paper puts forward one method to identify the age of wheat by employing a voltammetric electronic tongue (VE-Tongue) combined convolutional neural networks (CNN) and wasserstein generative adversarial networks (WGAN). VE-Tongue was used to detect six kinds of wheat with different storage time, then the corresponding electronic tongue signals were acquired. An automatic feature extraction and classification recognition model of electronic tongue signal based on CNN structure was designed to solve the problems of large amount of information and difficult feature extraction of sensor array response signals. To solve the problem of insufficient training samples, WGAN was utilized to generate electronic tongue signals to improve the generalization ability of CNN model and avoid over-fitting problem. The results showed that the proposed method exhibited better classification performance compared with the deep learning models like AlexNet, VGG16 and the traditional machine learning models like random forest (RM) and extreme learning machine (ELM). The test set accuracy, precision, recall rate and F1-Score of the proposed model reached 0. 98, 0. 98, 0. 977 and 0. 988 respectively. This study found that the VETongue combined with CNN and WGAN could be a sensitive, reliable and effective detection method for identifying the amount of storage year of wheat, it can provide a new research way of thinking for the sensory recognition technology based on artificial intelligence.