Abstract: Currently, graph convolutional network (GCN) holds significant potential in processing graph data and other unstructured data. However, GCN faces challenges when dealing with densely connected graphs, as their node-based neighborhood information aggregation mechanism can lead to excessive smoothing, thereby weakening the expressive power of the network graph. The construction of sparse graphs can alleviate the phenomenon of excessive smoothing during the convolution process to some extent, but sparse graphs are prone to information loss and the sparsification process lacks unified standards, affecting the model’s consistency and interpretability. To address this, we propose element separation and holistic attention graph convolutional network framework (EH-GCN). This framework does not require the basis of sparse graphs and can learn the connectivity and node features of densely connected graphs separately. Moreover, it employs a global attention mechanism to integrate connectivity and node features, thus overcoming the limitations of traditional GCN frameworks in handling densely connected graphs and enhancing the feature expression capability of the network graph. We firstly construct fully connected brain networks on three brain imaging datasets: ADNI, ABIDE, and AIBL, verifying the effectiveness of the EH-GCN model in classifying densely connected graphs. Subsequently, the model is tested on the FRANKENSTEIN chemical molecular graph dataset, demonstrating its robust generalization capability. Additionally, the interpretability analysis of the framework aligns with previous neuropathological research, further substantiating the biological basis of the framework.