Relation extraction aims to extract semantic relationships between two specified named entities in a sentence. Because a sentence often contains several named entity pairs, a neural network is easily bewildered when learning a relation representation without position and semantic information about the considered entity pair. In this paper, instead of learning an abstract representation from raw inputs, task-related entity indicators are designed to enable a deep neural network to concentrate on the task-relevant information. By implanting entity indicators into a relation instance, the neural network is effective for encoding syntactic and semantic information about a relation instance. Organized, structured and unified entity indicators can make the similarity between sentences that possess the same or similar entity pair and the internal symmetry of one sentence more obviously. In the experiment, a systemic analysis was conducted to evaluate the impact of entity indicators on relation extraction. This method has achieved state-of-the-art performance, exceeding the compared methods by more than 3.7%, 5.0% and 11.2% in F1 score on the ACE Chinese corpus, ACE English corpus and Chinese literature text corpus, respectively.