Relation classification is an important se-mantic processing task in the field of nat-ural language processing (NLP). State-of-the-art systems still rely on lexical re-sources such as WordNet or NLP systems like dependency parser and named entity recognizers (NER) to get high-level fea-tures. Another challenge is that important information can appear at any position in the sentence. To tackle these problems, we propose Attention-Based Bidirectional Long Short-Term Memory Networks(Att-BLSTM) to capture the most important se-mantic information in a sentence. The ex-perimental results on the SemEval-2010 relation classification task show that our method outperforms most of the existing methods, with only word vectors