As a feed forward neural network, broad learning system (BLS) has attracted much attention because of its high accuracy, fast training speed, and the ability to effectively replace deep learning methods. However, it is sensitive to the number of feature nodes and the pseudo-inverse method is likely to result in the problem of over fitting for BLS model. To address the above issues, Bayesian inference and graph regularization was introduced in to the BLS model. By introducing the prior knowledge for Bayesian learning, the sparsity of the weights and the stability of the model could be effectively improved; while the graph information mining from the data could be fully considered to improve the generalization ability of the model by regularization. The UCI and NORB dataset were adopted for evaluating the performance of the proposed model. The experiment results demonstrated that the proposed graph-regularized Bayesian broad learning system model can further improve the accuracy of classification and has better stability.