Ranking entities according to the relevance degree between the given entity and other entities in a Knowledge Graph (KG) is critical for related entity search. The relevance between entities is not only reflected in the KG but also the rapidly generated Web documents. In existing methods, the relevance degree is mainly calculated from the KG, which cannot reflect the knowledge rapidly evolving in the real world, and thus effective results cannot be obtained. Therefore, in this study, we first propose an algorithm for searching candidate entities on the basis of the TransH model by analyzing the semantic representation of entities in hyperplanes of different relations. To improve the precision of ranking candidate entities, we propose an Entity Undirected Weighted Graph (EUWG) model by quantifying the relevance between searched and candidate entities reflected in Web documents and KG. Experimental results show that the proposed method can precisely search and rank the candidate entities in the large-scale KG.