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In order to solve the problem that there are many restrictions on the source domain and the target domain in the process of Bayesian network parameter transfer， a unified framework based on Bayesian network parameter transfer learning was proposed under the condition of considering multiple information forms of source domain and target domain. The method considers the role of source domain structure and data volume in the migration. On the basis of structural similarity， the influence of alternative source domain data volume on parameter migration was discussed. The balance coefficient related to the target domain data was introduced in the migration process. According to the balance coefficient， the target domain data was linked with the migration process to realize the automatic adjustment of the balance coefficient. The Asia network verifies the accuracy of the method in this paper. [ABSTRACT FROM AUTHOR]