The current study of instance-transfer learning suffers from the mismatch between the granularities of data from multi-source heterogeneous domains. A Transfer Sparse unsupervised Hierarchical Probabilistic Self-Organizing Graph (TSHiPSOG) method based on the framework of Hierarchical Probabilistic Self-Organizing Graph (HiPSOG) method in the single domain was proposed. Firstly, representation vectors with different granularities were extracted from source and target domains by using hierarchical self-organizing model based on a probabilistic mixture of multivariate Gaussian component; and the sparse graph probabilistic criterion was used to control the growth of the model. Secondly, the most similar representation vector of the target domain data was searched in the rich-information source domain by using the Maximum Information Coefficient (MIC). Then, the data in the target domain was classified using labels of similar representation vectors in the source domain. Finally, the experimental results on the international universal 20 Newsgroups dataset and the spam detection dataset show that the proposed method improves the average classifying accuracy of target domain using the information from source domain by 15.26% and 9.05%. Moreover, the approach improves the average classifying accuracy with mining different granularity representation vectors by 4.48% and 4.13%.