<p>Graph-based methods have made significant progress in addressing the dependent correlations among ECG time series variables. However, most existing graph structures primarily focus on local similarity while overlooking global semantic correlation. Additionally, the adjacency matrix is highly susceptible to noise interference, leading to unreliable node connections. In this paper, we present a novel graph generation learning framework that incorporates semantic hash coding to capture the intricate associations both within and between ECG signals, thereby significantly enhancing the retrieval efficiency of subsequent graph-based deep learning models. Specifically, the Semantic Hash Similarity Graph (SHSG) initially leverages the similarity within the label space to generate a hash representation for the supervised signal. Subsequently, a lightweight linear hash function is utilized to produce a hash representation for the unseen signal. Thereafter, a comprehensive global hash dictionary is systematically constructed. Finally, the graph topology is meticulously assembled by leveraging Hamming similarity. Additionally, to ensure the maintenance of semantic similarity, we propose an iterative optimization approach in the orthogonal domain for generating hash representations. To validate the efficacy of the generated graph, we utilized a fast Graph Convolutional Network (GCN) for ECG recognition. The experimental outcomes on multiple publicly available ECG datasets corroborate the robustness and effectiveness of our proposed method.</p>