Taiping Houkui Tea, one of China's precious tea series, occupies an important position in public consumption and the tea market. The price of Taiping Houkui tea varies greatly from different geographical origins, and accurate geographical origin discrimination is currently an important factor in promoting the green tea industry. Sensory evaluation methods that rely on the manual experience are highly subjective and poor instability and cannot be applied to the actual analysis process. As the main detection and analysis method at present, the chemical analysis method is time-consuming and laborious. More importantly, there is currently no uniform standard for the geographical origin discrimination of tea. Near-infrared spectroscopy (NIR), as non-destructive testing and analysis method, has the characteristics of fast, non-destructive, and non-polluting. However, the types and contents of main components of Taiping Houkui tea from different origins are similar, which results in the same spectral peak distributions of various samples, and conventional analysis methods are limited for selecting feature variables. As one of the typical deep learning network models, convolutional neural network (CNN) has strong feature extraction and model expression capabilities. Based on the analysis of the spectral characteristics of Taiping Houkui tea from different geographical origins, the 1-dimension CNN (1-D CNN) is used to extract the NIR features, and a discriminant method combing NIR with 1-D CNN is explored to identify the geographical origin of Taiping Houkui tea in this work. In this paper, 120 samples were collected from 6 different geographical origins. The NIR were sampled from 10 000~4 000 cm^-1 and preprocessed by standard normal variate (SNV). The sample is randomly divided into a training set (84, 70%) and test set (36, 30%), and the effects of CNN structure, convolution kernel size, activation function and other parameters on the analysis results were discussed separately. As a result, a 1-D CNN model with 9-layer was constructed for the geographical origin discrimination of Taiping Houkui tea. The principal component analysis (PCA) was compared, and the Monte-Carlo method was used to evaluate the stability and robustness of the proposed method. Compared with the prediction accuracy and standard deviation of the models based on original spectral data (40.57%, 7.06) and the PCA method (31.93%, 6.96), the prediction accuracy and stability of the 1-D CNN-based geographical origin discrimination model are higher, and the average prediction accuracy and standard deviation of the testing set are 97.73% and 3.47, respectively. The comparison results demonstrate the proposed 1-D CNN model can effectively extract NIR features and has the ability to identify the geographical origins of Taiping Houkui tea, which provides an effective method for the identification and traceability analysis of the geographical origin and production of valuable tea products such as Taiping Houkui tea.