Electrocardiogram (ECG) is an electrical signal that helps monitor the physiology of the heart. A complete ECG record includes 12 leads, each reflecting features from a different angle of the heart. In recent years, various deep learning algorithms, especially convolutional neural networks (CNN), have been applied to detect ECG features. However, the conventional CNN can only extract the local features and cannot extract the data correlation across the leads of ECG. Based on deformable convolution networks (DCN), this article proposes a new neural network structure (DCNet) to detect ECG features. The network architecture consists of four DCN blocks and a classification layer. For the ECG classification task, in a DCN block, the combination of normal convolution and deformable convolution with better effect was testified by the experiments. Based on the feature learning capability of DCN, the architecture can better extract the characteristics between leads. Using the public 12-leading ECG data in CPSC-2018, the diagnostic accuracy of this architecture is the highest, reaching 86.3%, which is superior to other common network architectures with good results in ECG signal classification.Clinical relevance-In this paper, we proposed an effective automatic ECG classification model that can reduce medical staff workload.