Although deep transfer learning has made significant progress, its "black-box" nature and unstable feature adaptation remain key obstacles. This study proposes a multi-stage deep transfer learning method, called XDTL, which combines explainable feature extraction and domain reconstruction to enhance the performance of target models. Specifically, the study first divides features into key and regular features through cross-validation and explainability analysis, then reconstructs the target domain using a seed replacement method based on key target samples, ultimately achieving deep transfer. Experimental results show that, compared to other methods, XDTL achieves an average improvement of 27.43���% in effectiveness, demonstrating superior performance and stronger explainability. This method offers new insights into addressing the explainability challenges in transfer learning and highlights its potential for broader applications across various tasks.
