A literature retrieval system, which returns user papers domain-related with queries and ranks papers by importance, can help users quickly learn one academic domain. This paper develops a framework for the domain-oriented literature retrieval, which combines links and contents analysis to search and rank important papers in one academic domain. This framework designs a score function that evaluates both importance of the paper and its relevance to the domain. The study first proposes a community-core discovery algorithm, which is capable of finding a collection of papers domain-related with query from citation network and calculates an importance score for each paper. To assign other papers a domain-related score, a supervised non-negative matrix factorization method, using identified domain-related paper as prior knowledge, is also developed. The experiments conducted on synthetic and real datasets demonstrate the feasibility and applicability of this framework.