In light of strides in Artificial Intelligence (AI) and its wide-spread application, challenges persist in the interpretability of AI models, particularly within specialized domains like healthcare, such as electrocardiograph (ECG) recognition. Rather than relying solely on end-to-end convolutional neural networks, this paper introduces a novel approach using a domain knowledge-based graph convolution network for ECG recognition. Key landmarks points of PRQST, vital to ECG interpretation, are incorporated as domain knowledge. The double-stream directed graph is employed to model both intra and inter ECG cycles. Specifically, spatial directed graphs capture the positional relationships among key points, while temporal directed graphs delineate temporal dependencies between adjacent cycles in extended ECG sequences. Experimental results on the First Chinese ECG Intelligent Competition dataset, which specifically classify ECG into nine categories, prove the efficacy of the proposed model. The overall average F1 score is 88.1%, the average F1 score of rare categories is 76.3%, both outperform the state-of-the-art models. The introduction of domain knowledge did enhance the detection performance, especially for rare categories.