The process of relation extraction is a significant step in several information extraction systems designed to mine structured facts from text. However, two problems surface when traditional distant supervision methods are employed to conduct the entity relation extraction task. First, the distant supervision heuristic aligns the text in the corpus using existing knowledge marked with entities and relations, after which the alignment results are treated as annotation data; this leads to inevitable labeling errors. Second, current statistical methods rely extensively on natural language processing tools to extract features, and the noise accumulating during the entire process significantly affects the extraction results. In this study, an end-to-end, attention mechanism-based convolutional neural network (CNN) is proposed. First, the attention mechanism is added to the input layer for automatic detection of more subtle clues and learning of parts of sentences that are relevant to relation extraction. Second, the sentence is encoded based on the position feature and word feature, a piecewise CNN (PCNN) is used to extract sentence features and classify relationships, and finally a max-margin loss function with a higher efficiency is used on the network. The accuracy of this method when used on the New York Times dataset is 2.0% higher than that of the classical PCNN+MIL model, and 1.0% higher than that of the classical APCNN+D model. The experimental results therefore demonstrate excellent accuracy of the proposed model when compared with that of other baselinemodels.