隨著全球資訊網的發展，人們享受了資訊快速流通的便利，也造就了搜尋引擎的發展。針對學術文獻，ACM, IEEE等學術組織也將學術文獻數位化，並提供關鍵字查詢文獻的功能。此外，Google也發展了Google Scholar搜尋全球資訊網上的學術文獻。Google在回傳查詢結果時，除了考慮文獻內容與查詢關鍵字的相似度之外，也利用PageRank技術來考量文獻間的引用關係。但是，有時後使用者想查詢的是與查詢相關的重要參考文獻。這些文獻的內容與查詢未必有很高的相似度。 因此本論文的研究目的在研究並發展推薦重要參考文獻的技術。我們先利用蜘蛛程式( spider)與剖析程式( parser)擷取分析ACM Digital Library上所收錄的論文後設資料，並解析出論文篇名、作者、摘要、關鍵字、分類、參考文獻等論文的重要組成要素。接著利用Mixed Media Graph（MMG）以描述關鍵字與參考文獻間關係的MMG 模型。當使用者輸入關鍵字，利用MMG做random walk因此可以找出與輸入關鍵字相關性最高的參考文獻。 / The rapid development of the Internet, people enjoy the rapid flow of information to facilitate, but also created a search engine of development. ACM and IEEE have developed the digital libraries to provide literature search. Moreover, there exist some search engines for academic literature, such as Google Scholar. Google Scholar collects academic literatures from WWW and provides users the capability to query literatures by keywords. However, sometimes what users need is to search for important citations specified by authors, such as seminal survey papers or books. The aim of this thesis is to investigate and develop the mechanism for search for important citations. In the developed mechanism, first the spider crawls and collects the literature from ACM Digital Library. Then the parser parse and extract the meta information for each literature. The Mixed Media Graph is employed to capture the relationships between keywords and citations. Given a set of query keywords, the important citations are generated by random walk over the constructed Mixed Media Graph. Performance analysis shows that the proposed mechanism performs well.