Abstract: Using the papers in computer science extracted from Scholat as the corpus, multiple word vector training schemes are proposed using the Glove semantic toolkit, and their performances are compared and analyzed. Then, a random projection method is proposed to quickly access vectors in the large vector space. Finally, a semantic vector computing scheme for the whole academic documents is proposed based on the word vector representations. A series of experiments are conducted, and the effectiveness of the proposed scheme word vector based academic semantic search is verified. This scheme is applied to the search function of Scholat and it can obtain satisfying performance.