The　problem　solving　performance　of　a　case-based　reasoning　(CBR)　system　is　closely　related　to　the　quantity　and　quality　of　the　cases　stored　in　the　case　base.　With　the　continuous　growth　of　the　size　of　the　case　base,　the　so　called　＂swamping　problem＂　may　occur　when　the　time　cost　of　retrieval　exceeds　the　benefit　of　the　accuracy.　From　the　perspective　of　cognitive　science,　a　dynamic　maintenance　method　improved　by　selective　memory　and　intentional　forgetting　for　CBR　is　proposed,　which　can　imitate　the　memory　function　of　the　human　brain　to　selectively　save　new　cases,　update　the　forgotten　values　and　intentionally　delete　the　old　cases.　The　experiments　show　the　effectiveness　of　the　proposed　method.　The　selective　memory　and　international　forgetting　policy　can　significantly　reduce　the　time　and　space　complexity,　and　retain　or　improve　the　accuracy　of　the　CBR　classifier,　thus　improving　the　performance　of　CBR.　(C)　2014　Elsevier　Inc.　All　rights　reserved.