To address the issues of slow decoding speed in large-scale networks and insufficient utilization of feature information in decoding Motor Imagery ElectroEncephaloGram （MI-EEG）， a lightweight MI-EEG decoding neural network with multi-domain feature fusion was proposed. In the proposed network， lightweight modules were introduced to extract multi-domain features， including the use of SincNet for frequency-domain feature extraction and the use of Temporal Convolutional Network （TCN） for time-domain feature extraction. Additionally， after extracting time-frequency domain features， the Squeeze-and-Excitation （SE） attention was incorporated to calibrate feature maps adaptively， thereby emphasizing important features and suppressing redundant information. Finally， separable convolution was employed to fuse time-frequency features effectively， thereby addressing the limitation of single-domain feature information. Furthermore， a joint loss function combining cross-entropy and center loss was adopted to constrain network training， thereby optimizing both intra-class and inter-class classification performance. Experimental results showed that on the Motor Imagery （MI） public datasets BCI 2a， SMR-BCI， and OpenBMI， the proposed network had the parameter counts of 6 870， 5 690， and 6 870， respectively， the average accuracies of 74.78%， 71.93%， and 65.40%， respectively， and the average Kappa values of 0.70， 0.66， and 0.59， respectively； Compared to Deep Convolutional Network （DeepConvNet）， lightweight EEG convolutional neural Network （EEGNet）， and Temporal Convolutional Network-based EEG Recognition （EEG-TCNet）， the proposed network achieved average accuracy improvements of 11.06， 8.85， and 6.36 percentage points on the BCI 2a dataset； 10.53， 4.17， and 3.57 percentage points on the SMR-BCI dataset； and 5.09， 4.99， and 2.33 percentage points on the OpenBMI dataset， respectively. The results demonstrate that the proposed network ensures lightweight design while maintaining robust decoding performance.