In order to realize the rapid traceability detection of coffee origin, a detection method based on the combination of electronic tongue and generative adversarial networks (GAN) - convolutional denoising autoencoder (CDAE) – extreme learning machine (ELM) is proposed. Aiming at the problems of low accuracy and poor generalization ability of deep learning model caused by the insufficient number of original data samples of electronic tongue detection, generative adversarial networks (GAN) is used to expand the data scale of training samples and improve the robustness of the system; According to the characteristics of complex electronic tongue output signal, large dimension and many noise, convolutional denoising autoencoder (CDAE) is used to extract the features of electronic tongue signal in low dimensional feature space to improve the expression ability of key features; Finally, extreme learning machine (ELM) is used to classify and identify the extracted feature information, and a coffee origin traceability detection and analysis model is constructed. The results show that compared with the traditional machine learning models such as discrete wavelet transform (DWT), support vector machine (SVM) and extreme learning machine (ELM), as well as the deep learning models such as VGG16 network, GAN-CDAE-ELM has better discrimination effect on coffee from different producing areas, and its test set has higher accuracy, precision, the recall and F1 score reached 99.00%, 99.03%, 99.00% and 0.9901. This study provides a new idea for rapid identification and detection of coffee producing areas based on intelligent sensory system.