Bibliography information network is a typical heterogeneous information network and the similarity search based on it is a hot topic of graph mining. However, current methods mainly adopt meta path or meta structure to search similar objects, do not consider semantic features of node itself which leads to a deviation in the search results. To fill this gap, a vector-based semantic feature extraction method was proposed, and a vector-based node similarity calculation method called VSim was designed and implemented. In addition, a similarity search algorithm VPSim (Similarity computation Based on Vector and meta Path) based on semantic features was designed by combining the meta-paths. In order to improve the execution efficiency of the algorithm, a pruning strategy based on the characteristics of bibliographic network data was designed. Experiments on real-world data sets demonstrate that VSim is applicative for searching entities with similar semantic features and VPSim is effective, efficient and extensible.