To realize the non-destructive identification of Panax notoginseng powder in different parts, we propose a non-destructive identification method based on the electronic nose and time-domain feature extraction. First, The electronic nose technology combined with statistical analysis method was used to collect and extract nine time-domain characteristics of the response information of Panax notoginseng whole root powder, tap root powder, rhizome powder, and fibrous powder, including the data at 110s, the mean value between 101-120s, the maximum value, minimum value, integral value, differential value, skewness factor, kurtosis factor, and standard deviation between 0-120s. Next, three classical feature selection method were used to reduce the data dimension. Subsequently, the classification models of support vector machine (SVM), least-square support vector machine (LSSVM), and extreme learning machine (ELM) were established based on original data, multi-feature data, and feature selection data. Finally, the Grey Wolf Optimization (GWO) algorithms were introduced to optimize the parameters of the classification model. The results show that the GWO-CARS-LSSVM achieved the best modeling effect, and the classification accuracy on the test set was 97.92%.Therefore, This study provides a theoretical basis and technical support for rapid identification of adulteration of Panax notoginseng powder.