Source-free domain adaptation (SFDA) aims to transfer knowledge from the source domain to an unlabeled target domain without requiring access to source data. Although previous works have focused on clustering target domain samples from continuous training, there are still some challenges: i) More source domain knowledge is forgotten with more training epochs. ii) Achieving better learning results often requires increased computational resources. To solve these problems, we propose a novel Marginal Exploration for Source-Free Domain Adaptation (ME-SFDA) method, which is a multi-scale information fusion learning based on our designed Pyramidal Atkinson-Shiffrin memory. Specifically, we design a two-step module to split samples into clustered cores and response scatters by sensory memory. Then, a novel technique is proposed for clustering samples in a hierarchical way, utilizing long-term memory to cluster cores derived from splitting the samples earlier and guide response scatters. To effectively divide samples of different classes, we propose a method that encourages unambiguous cluster assignments for the samples using multi-scale fusion information. To verify the generality of our approach, we not only discuss the UDA and SFDA tasks but also apply it to the semi-supervised domain adaptation (SSDA), which utilizes a few labeled target samples based on UDA. Extensive experiments on four standard benchmarks indicate that our approach outperforms previous SOTA methods.