This study encodes distributional semantics into the triple-based background knowledge ranking model <cite>(Zhang et al., 2014)</cite> for better document enrichment. We first use LDA to represent every triple as a real-valued vector, which is used to evaluate the relatedness between triples, and then propose a modified iterative propagation model to rank all the triples of background knowledge. For evaluation, we conduct two series of experiments: (1) evaluation as ranking problem, and (2) taskbased evaluation, especially for document classification. In the first set of experiments, our model outperforms multiple strong baselines based on VSM, LDA, and WE. In the second set of experiments, our full model with background knowledge outperforms the state-of-the-art systems significantly. Moreover, we also explore the impact of knowledge quality and show its importance.