To address the limitations in model decoding performance caused by the low spatial resolution and high inter-subject variability of motor imagery EEG signals, this paper proposes a novel DBTNet model based on an attention-enhanced dual-branch convolutional network and a temporal multi-scale attention mechanism. The model employs a dual-branch convolutional network to extract multi-scale spatiotemporal features and integrates an efficient multi-scale attention mechanism to enhance the extraction of spatial features from EEG signals. Subsequently, a temporal multi-scale attention mechanism is applied to capture both local features and global dependencies under different receptive fields, thereby obtaining more comprehensive feature representations. Finally, a classifier is used to fuse the extracted features for efficient decoding. In subject-dependent evaluations, the proposed model achieves a four-class classification accuracy of 86.57% with a Kappa coefficient of 0.821 0 on the BCI Competition IV-2a dataset, and a two-class classification accuracy of 88.95% with a Kappa coefficient of 0.779 0 on the BCI Competition IV-2b dataset. Experimental results demonstrate that the DBTNet model achieves superior model decoding performance.