Early after the establishment of the patent system, the amount of patents is increased dramatically year by year. The first and most problem to those who need to do patent search, especially those novice searcher, is the emerging technologies and domain knowledge which are very complicated and difficult to catch up with. Therefore the experience on patent search and the domain knowledge have become a scarce competence for patent examiners, patent engineers, R&D engineers and others who need to search patent constantly. A qualified patent analyst should have the ability of information search, domain professions and legal knowledge. Unfortunately, it was a time-consuming effort for searchers to find out the patents that they really want. Therefore, automatic tools for assisting patent engineers or decision makers in patent analysis are in great demand. For an automatic tool to come out the best search result, it is necessary to establish patent keyword dictionary to make the patent analysis become a common skill to each industrial domain. But for the researcher’s knowledge, for those patent management systems available on the market, all of them have lack of domain patent keyword dictionaries. The main reason for this is because the construction of the dictionaries is very time consuming and labor intensive task and must be done by the domain experts themselves. The objective of this research is to test the feasibility of an automatic method to development the patent keyword dictionary. The method integrated bibliographic data in patent records with text mining technique to identify domain keywords. To demonstrate the feasibility, the proposed method was applied to a real-world patent set for domain analysis and mapping, which shows that our approach did not generate more accurate and stable results compare to the original pure text mining method. Therefore, more researches will be needed to further clarify the feasibility of the method proposed by this research.