Relation classification is an important semantic processing task in the field of natural language processing. The traditional relation classification method judges the relationship between two entities within a sentence by manually designing various features and various kernel functions. In recent years, the main work of the relation classification methods has focused on obtaining semantic feature representations of sentences through various neural networks to perform classification, so as to reduce the manual construction of various features. In sentences, the contribution of different keywords to the relation classification tasks are different, but the most important word meanings may appear at any position in the sentences. To this end, we propose an attention-based hybrid neural network relation classification method to capture the important semantic information for relation classification. This method is an end-to-end method. Experimental results show the effectiveness of the method.