<p>Accurate traffic flow prediction is crucial for the development of intelligent transportation systems aimed at preventing and mitigating traffic issues. We present an information-enhanced spatio-temporal graph neural network model to predict traffic flow, addressing the inefficient utilization of non-Euclidean structured traffic data. Firstly, we employ a multivariate temporal attention mechanism to capture dynamic temporal correlations across different time intervals, while a second-order graph attention network identifies spatial correlations within the network. Secondly, we construct two types of traffic topology graphs that comprehensively describe traffic flow features by integrating non-Euclidean traffic flow data, regional traffic status information, and node features. Finally, a multi-graph convolution neural network is designed to extract long-range spatial features from these traffic topology graphs. The spatio-temporal feature extraction module then combines these long-range spatial features with spatio-temporal features to fuse multiple features and improve prediction accuracy. Experimental results demonstrate that the proposed approach outperforms state-of-the-art baseline methods in predicting traffic flow performance.</p>