Multi-attribute data privacy publication fails to balance the difference in attribute sensitivity and computational efficiency. For this reason, HMPrivBayes, a heterogeneous multi-attribute data publishing method with differential privacy based on attribute segmentation, is proposed. Firstly, the spectral clustering algorithm satisfying differential privacy is designed to segment the original data set, in which the similarity matrix is generated by the attribute maximum information coefficient. Secondly, with the help of attribute information, this method uses an improved Bayesian network construction algorithm to build Bayesian networks for each data subset. Finally, HMPrivBayes adds heterogeneous noise disturbance to the attribute joint distribution extracted from the Bayesian network to realize the protection of heterogeneous multi-attribute data, in which privacy budget is allocated based on the normalized risk entropy of attribute. The experimental results show that HMPrivBayes not only reduces the added noise but also improves the computational efficiency of synthetic data.