The Application Of Co-occurrence Analysis In The Recognition Of The Features Of Discipline Intersecting

This paper analyzes the domestic and foreign research status of interdisciplinary characteristics, although a lot of methods of cross disciplinary traits, the main journal co-citation analysis, author co-citation analysis, co-word analysis, subject analysis, direct citation analysis, author co-occurrence analysis, but the domestic and foreign scholars in the use of these methods of discipline use only a single feature, and not with the interdisciplinary feature identification index, did not form a system, so the understanding of the cross disciplinary research is not deep enough, not fully displayed the interdisciplinary feature.In this paper,many scholars research on the subject, using cluster analysis, journal co citation analysis, co-word analysis, the author co-occurrence analysis, combined with the method of social network analysis software such as bibexcel, Ucinet, Netdraw associated network between disciplines, and then use the the index of interdisciplinary feature recognition, groups of core periphery structure index, diversity index, the influence of subject index, to identify the interdisciplinary characteristics, so that itformat the discipline system, the intersection of science of system in the biomedical engineering.A more detailed description of the application of co-occurrence analysis in the interdisciplinary feature recognition in the study, to verify the practicability of interdisciplinary characteristics of the system, at the end of the article, we also compare the several co-occurrence analysis method to do a simple comparison, points out the advantages of each method and limitations, to do a very good reference for the optimization of interdisciplinary characteristics of the system.The article has the important theory significance to the research results in the interdisciplinary research method and it also has the important practical significance for the research objective, comprehensive, systematic cross characteristics.