Most motor imagery-based brain-computer interface studies rely on single-scale feature extraction methods， which use convolutional or recurrent structures with fixed receptive fields and struggle to comprehensively capture the temporal characteristics of electroencephalogram （EEG） signals. Aiming at the above problem， we propose a multi-scale dilated convolutional network （MSDCN） model for motor imagery EEG signal classification. The proposed model first extracts spatiotemporal features through two layers of one-dimensional convolution， then enhances its ability to model both short-term dynamic variations and long-term dependencies using multi-scale dilated convolutions. Additionally， a squeeze-and-excitation （SE） module is integrated to learn channel-wise feature importance， highlighting key feature channels and improving classification performance. Experimental results show that on the BCI Competition Ⅳ 2a dataset， the model achieves an accuracy of 84.1% for within-subject experiments and 69.1% for cross-subject experiments， and on the BCI Competition Ⅳ 2b dataset， the within-subject accuracy reaches 89.8%.Highlights：1. A novel multi-scale dilated convolutional network （MSDCN） is proposed for motor imagery EEG classification， which adopts four parallel branches with different convolution kernel sizes （1×3， 1×5， 1×7， 1×9） to synchronously capture short-term local temporal features and long-range global dependencies of EEG signals.2. A descending dilation rate strategy （d=8， 4， 2） is designed inside each dilated convolution block， which models global temporal information first and then supplements local details， effectively alleviating the sparse feature loss problem caused by large dilation rates.3. The squeeze-and-excitation （SE） channel attention module is embedded in each multi-scale branch to adaptively assign weights to feature channels， strengthening discriminative EEG channels related to motor imagery tasks and suppressing irrelevant interference features.