To　address　the　bottleneck　problems　of　case　adaptation　knowledge　acquisition　and　learning　and　the　difficulty　of　simultaneously　applying　the　network　structure　to　multi-attribute　case　representation,　this　paper　proposes　applying　deep　reinforcement　learning　(DRL)　to　the　learning　of　case　difference　heuristic　(CDH)　adaptation　knowledge　and　implementing　the　generation　process　of　a　case　adaptation　solution　based　on　the　＂learningevaluation-revision＂　idea.　The　method　first　establishes　the　connection　between　DRL　and　the　CDH　adaptation　method　and　then　introduces　the　corresponding　principles.　Next,　the　CDH　adaptation　algorithms　of　deep　Q　networks　(DQN)　and　deep　deterministic　policy　gradient　(DDPG)　are　given.　The　＂evaluation-revision＂　process　of　adaptation　is　implemented　according　to　the　intelligent　agent-environment　mechanism　of　DRL.　Finally,　experimental　verification　is　carried　out　on　public　datasets　and　actual　solid　waste　data.　The　results　show　that　the　proposed　method　can　effectively　adjust　case　solutions　to　adapt　to　new　problems,　significantly　improving　the　problem-solving　quality　of　case　reasoning　and　achieving　good　effects　in　actual　applications.