Tibetan medicine has a long history as a traditional ethnic medicine in China. It is playing an important role in the medical system in northwestern of China, and which has attracted more and more attention due to its unique diagnostic system and clinical efficacy. Meanwhile, as the data mining technology has been widely used in traditional Chinese medicine (TCM), its application in the field of Tibetan medicine has also launched preliminarily. In this project propose,  Chronic Atrophic Gastritis (CAG) which is a typical gastrointestinal disease in the plateau area, and a novel back-propagation (BP) network model is proposed for Tibetan medical syndrome classification and prediction. K-means clustering algorithm was firstly implemented on the diagnostic data which was obtained from the Qinghai Provincial Tibetan Hospital, and then Correlation-based Feature Selection (CFS) method was adopted for feature selection. The selected feature vectors were finally put into the proposed BP network for training and testing. In order to overcome BP network’s typical shortcomings including slow convergence and easy to over fit, we use a method based on Gaussian distribution to improve weights initialization, and dynamically adjusted the learning rate using the learning rate exponential decay method. Further, we add regularization to the loss function to prevent over fitting. Ultimately, the experiment achieved an accuracy of 99.09%, which improved significantly after improvement and achieved better result compared with other classification methods.