To solve the problems in turbine blade investment casting die design process such as long design time,lacking of expert experience and low level of intelligence,knowledge-based engineering (KBE) was introduced in the turbine blade investment casting die design field. The key technologies of the intelligent design method were researched and a prototype system was developed. A hybrid reasoning model was prompted in which case-based reasoning (CBR) was applied to conceptual design and rule-based reasoning (RBR) was applied to parts design after research the design process and domain knowledge of casting die. In the conceptual design stage,a retrieval model which integrated nearest neighbor approach and knowledge-based retrieval approach was prompted to improve the retrieval efficiency. Meanwhile,RBR was used to modify the retrieval result. The practical application results indicate that this system can reuse the expert experience efficiently and heighten the die design efficiency and quality. To solve the problems in turbine blade investment casting die design process such as long design time, lacking of expert experience and low level of intelligence, knowledge-based engineering (KBE) was introduced in the turbine blade investment casting die design field. The key technologies of the intelligent design method were researched and a prototype system was developed. A hybrid reasoning model was prompted in which case-based reasoning (CBR) was applied to conceptual design and rule-based reasoning (RBR) was applied to parts design after research the design process and domain knowledge of casting die. In the conceptual design stage, a retrieval model which integrated nearest neighbor approach and knowledge-based retrieval approach was prompted to improve the retrieval efficiency. application results indicate that this system can reuse the expert experience efficiently and heighten the die design efficiency and qual ity.