Entity Linking (EL) refers to the task of linking entity mentions in the text to the correct entities inthe Knowledge Base (KB) in which entity embeddings play a vital and challenging role because of the subtle differences between entities. However existing pre-trained entity embeddings onlylearn the underlying semantic information in texts yet the fine-grained entity type informationis ignored which causes the type of the linked entity is incompatible with the mention context. In order to solve this problem we propose to encode fine-grained type information into entity embeddings. We firstly pre-train word vectors to inject type information by embedding wordsand fine-grained entity types into the same vector space. Then we retrain entity embeddings withword vectors containing fine-grained type information. By applying our entity embeddings to twoexisting EL models our method respectively achieves 0.82% and 0.42% improvement on average F1 score of the test sets. Meanwhile our method is model-irrelevant which means it can helpother EL models.