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In classification problem, Bayesian networks play an important role because of its efficiency and interpretability. Bayesian networks learning methods require enough data to produce reliable results. Unfortunately, in practice, the training data are often either too few, expensive to label, or easy to be outdated. However, there may be sufficient labeled data that are available in a different but related domain. Learning reliable Bayesian networks from limited data is difficult; and transfer learning might be used to improve the robustness of learned networks by combining data from auxiliary and related labeled dataset. In this paper, we propose a novel transfer learning method for Bayesian networks for classification that considers both structure and parameter learning. Our solution is to first construct the initial Bayesian networks model for auxiliary labeled data, and then revise the model according to an Expectation-Maximization (EM) algorithm, structure and parameters are revised by turns, in order to make it applicable to the target unlabeled dataset. We mainly apply our method on a special type of Bayesian networks, namely tree-based Bayesian network. To validate our approach, we evaluated the method on a real and typical classification scenario - text classification problem. We compared our method with other transfer learning method as well as the traditional supervised and semi-supervised learning algorithms. The experimental results show that our algorithm is very effective and obtains a significant improvement when we transfer knowledge from related dataset.