| At the beginning of the 70's of the 20th century the American M. S .Scott Morton put forward the concept of the decision support system in his 《Management Decision System》firstly. Decision Support system in fact is developing on the basis of management information system and operations research. Its main purpose is faced to high decisions. And with it we can solve half-structured and unstructured decisions in management. Decision support systems adopt the technology of database and model base so that man-machine conversation is convenient. Decision support system is to describe and solve complicated problems. If the model base in DSS only have data model, it will be limited by the data model's expression capability and conditions and can not provide powerful support for non-formulary or unstructured decisions. So we should adopt not only data model but also knowledge model and logic model etc. Expert system can be bring into DSS to improve problem describe capability and solving intelligence. At the same time, because the decision-maker is seldom professional in computer the interface must be friendly, intuitionistic, convenient, and lively. Hence, in the 1980's intelligent decision support system emerge with tide of times. IDSS is a combination of DSS and AI. For its special research method and vast developing space, it becomes the hotspot and developing direction in DSS. In the last 20 years, enormous advances have been obtained in intelligent decision support system application research. For most of the decision problems are unstructured problems, traditional reasoning method in ES can not provide intelligent support for the decision process. The learning activities are static and passive in the traditional DSS with ES. They don't establish dynamic learning strategy according to actual conditions and lack active research mechanism. That limited the flexibility and adaptability of DSS. In addition, the computer can not accurately and duly understand asking and demands of user. As the same time, user also can not add in dynamitic inspired information. There is insufficiency in method by general knowledge. Firstly, we must face the bottleneck of knowledge obtaining. Sometimes, its cost is too high. Sometimes, though the experts know how to do and what to do, but they can not sum up common rules. Secondly, the fragility can not be avoided. Once the knowledge to handle problems is beyond scope of knowledge base, the system is incapable. For the knowledge base is not complete, so the fragility is the inherent limitation. Aim at the shortage of the above traditional intelligent decision support system, we bring into the technology of Case-based Reasoning. CBR that rising in 1990's match to human perception mentality and avoid the difficulties such as obtaining rules, misunderstanding, missing of information. So in the field where knowledge is hardly to be express but a lot of experiences have been obtained, CBR have been applied widely such as the law consultation, the medical diagnosis, engineering design and planning etc. My paper primarily researches the application of CBR in IDSS. In this paper we have discuss CBR in retrieve, reuse, revise, review, return. Firstly, we design an IDSS model based on CBR and discuss its key technique. Secondly, we put forward a similar case retrieve arithmetic based on nerve network and a similar case revise arithmetic based on Rule-Based Reasoning and causality. The former arithmetic is based on three layer BP network model and has the advantage to organize and adapt by itself. We have no need to define similarity and weigh among cases. The latter arithmetic that is based on causality and RBR is to solve the problem of rules' revising. It has avoided rewriting rules in case, synthesizing the reasoning mechanisms of CBR, RBR and causality model. It has realized organizing reasoning by itself and improved the credibility of the conclusion and canning be explained. Though we have researched application of CBR in IDSS especially some theories and mo...
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