Traffic flow prediction serves as a core component of intelligent transportation systems, rendering it indispensable for efficient traffic management and planning. To mitigate the limitations of existing methods in graph structure construction and attention computation processes, a traffic flow forecasting model that Integrating NAGC-FLA (node-adaptive graph convolution with focused linear attention) was proposed. The proposed model was primarily composed of three modules: a node-adaptive graph convolution module, a spatial focused linear attention module, and a temporal attention module. Initially, dynamic spatio-temporal features were extracted via embedding, and adaptive road network information was constructed to facilitate the learning of road network node features. Subsequently, the spatial focused linear attention module adopts linear attention to reduce computational complexity, while a focusing function was introduced to enhance feature extraction performance. Furthermore, the temporal attention module was utilized to capture non-linear temporal dependencies. Finally, node-adaptive graph convolution was employed to capture the unique variation patterns of each node, and gated fusion is performed with the output of the attention modules to dynamically adjust the weights assigned to the two types of outputs. Experimental validations are conducted on three real-world traffic datasets. In comparison with the optimal baseline model, the proposed model exhibits significant improvements in prediction accuracy: the three key evaluation metrics, namely mean absolute error, root mean square error and mean absolute percentage error, are reduced by an average of 2.49%, 1.42%, and 3.72%, respectively.