To　improve　the　learning　performance　of　the　revision　stage　in　case-based　reasoning　(CBR),　an　attribute　difference　revision　method　(ADR)　is　proposed　in　this　paper.　First,　the　suggested　solution　of　the　target　case　is　obtained　through　the　case　retrieval　and　case　reuse;　then,　the　revision　value　of　the　suggested　solution　and　output　results　of　the　CBR　model　are　obtained　by　using　the　support　vector　regression　(SVR)　model,　which　is　based　on　the　difference　between　the　target　case　and　similar　cases;　finally,　the　target　case　and　its　correct　solutions　are　stored.　Experiments　and　applications　shows　that　the　ADR　method　is　effective　and　the　fitting　error　of　the　ADR-based　CBR　(ADRCBR)　model　is　significantly　lower　than　other　typical　regression　methods,　indicating　that　ADR　can　improve　the　learning　performance　of　the　CBR　model　and　has　the　advantage　of　application.　(C)　2017　Elsevier　Ltd.　All　rights　reserved.