Abstract: In many machine learning algorithms, a major assumption was that the training samples and the test samples had the same distribution. However, this assumption did not hold in many real applications. In recent years, transfer learning had attracted a significant amount of attention to solve this problem. Among these methods, an effective algorithm based on clustering analysis and resampling could correct different types of domain differences and did not need to estimate the different distribution directly. As the critical part, the original clustering algorithm was not good enough at data structure exploration due to its poor robustness on the data with various shapes and densities. In this paper, a new transfer learning algorithm based on fuzzy neighborhood densitybased clustering and resampling was proposed, which was more robust to datasets with various shapes and densities, and could explore more data structure information. With the better explored data structure information, the proposed method could transfer more useful knowledge from source domain to target domain. Validation of the proposed method was performed with extensive experiments. Results demonstrate that the proposed method can more effectively and stably enhance the learning performance.