In response to the limitations of deep learning approaches in motor imagery classification using electroencephalogram （EEG） signals， such as the failure to explore inter-channel correlations and fully exploit frequency， temporal， and spatial information， this study proposes a classification method named NTEEGNet， which combines nonnegative matrix factorization （NMF） with temporal convolutional network （TCN） and one compacted convolutional neural network named EEGNet to enhance the performance of motor imagery classification with a relatively small number of parameters. The NMF component of the model effectively extracts channel features and fully utilizes frequency， temporal， and spatial information. Additionally，the network’s receptive field increases exponentially under the action of TCN， leading to stronger feature extraction capabilities with fewer parameters. Experimental results on the BCI Competition Ⅳ 2a dataset demonstrate that NTEEGNet can achieve an impressive classification accuracy of 83.99%， improved by 6.64% on the basis of EEG-TCNet.