—In recent years, research on hyperspectral image (HSI) classification has continuous progress on introducing deep network models, and recently the graph convolutional net- work (GCN) based models have shown impressive performance. However, these deep learning frameworks are based on point estimation suffering from low generalization and inability to quantify the classification results uncertainty. On the other hand, simply applying the distribution estimation based Bayesian Neural Network (BNN) to classify the HSI is unable to achieve high classification efficiency due to the large amount of parameters. In this paper, we design a Bayesian layer as an insertion layer into point estimation based neural networks, and propose a Bayesian layer graph convolutional network (BLGCN) model by combin- ing graph convolution operations, which can effectively extract graph information and estimate the uncertainty of classification results. Moreover, a Generative Adversarial Network (GAN) is built to solve the sample imbalance problem of HSI dataset. Finally, we design a dynamic control training strategy based on the confidence interval of the classification results, which will terminate the training early when the confidence interval reaches the presented threshold. The experimental results show that our model achieves a balance between high classification accuracy and strong generalization. In addition, it can quantify the uncertainty of outputs.