For quality evaluation of Gastrodia elata f. glauca (GEFG), it's crucial to develop an analytical method to determine the geographical origin. Herein, 371 GEFGs are collected from five provinces, focusing on analysis of dry matter content (DMC), origin identification, geographical indication (GI) production area discrimination by using a combination of Fourier transform infrared (FTIR) spectroscopy and deep learning, data driven version of soft independent modeling of class analogy (DD-SIMCA). A significant difference in DMC of GEFG between Yunnan and other origins, which may be related to precipitation, altitude, temperature, and soil. The residual neural network (ResNet) model based on synchronous two-dimensional correlation spectroscopy (2DCOS) images has stable performances, its accuracy is 100%. The DD-SIMCA model can differentiate GI production areas of GEFG, while for non-GI areas, the model specificity is 71.38%. This study provides a promising approach for GEFG geographical traceability and GI production area differentiation.