To understand trends in a specific research field, qualitative literature analysis of major papers published in that field has traditionally been used. Recently, there has been an increasing effort to quantitatively analyze large volumes of literature using text analysis techniques. This study proposes an analysis method utilizing word embedding and entropy. The proposed method is applied to analyze literature in the field of Management Information Systems (MIS). For this purpose, 12,704 papers published in major MIS journals were collected. Word2Vec and annual keyword clustering were applied to 7,813 keywords extracted from these papers, and the research trends in the MIS field were analyzed through vector similarity analysis. Furthermore, research topics were visualized and classified according to their life cycles based on the rate of entropy change and average values. The method proposed in this study is applicable to various research fields and holds significance as a quantitative approach to analyzing large volumes of literature.