As the number of academic papers and new technologies soars, it has been increasingly difficult for researchers, especially beginners, to enter a new research field. Researchers often need to study a promising paper in depth to keep up with the forefront of technology. Traditional Query-Oriented study method is time-consuming and even tedious. For a given paper, existent academic search engines like Google Scholar tend to recommend relevant papers, failing to reveal the knowledge structure. The state-of-the-art MapOriented study methods such as AMiner and AceMap can structure scholar information, but they’re too coarse-grained to dig into the underlying principles of a specific paper. To address this problem, we propose a Study-Map Oriented method and a novel model called RIDP (Reference Injection based Double-Damping PageRank) to help researchers study a given paper more efficiently and thoroughly. RIDP integrates newly designed Reference Injection based Topic Analysis method and Double-Damping PageRank algorithm to mine a Study Map out of massive academic papers in order to guide researchers to dig into the underlying principles of a specific paper. Experiment results on real datasets and pilot user studies indicate that our method can help researchers acquire knowledge more efficiently, and grasp knowledge structure systematically.