In recent years, motor imagery (MI) has attracted significant attention in the fields of assistive healthcare and human-computer interaction. However, the classical common spatial pattern (CSP) feature extraction method is mainly based on calculating covariance matrices (CM) from time-domain signals, making it susceptible to noise and artifacts while failing to fully exploit the spectral information of electroencephalogram (EEG) signals. This limitation reduces classification accuracy and stability. To address this problem, this study proposes a MI-EEG classification algorithm based on regularized spectral covariance matrix (RSCM) and Riemannian space. Firstly, the preprocessed EEG signals undergo fast Fourier transform (FFT) to compute spectral covariance matrices, followed by ridge regularization. Then, the regularized matrices are mapped into the tangent space for geodesic filtering and projected back to the Riemannian space for CSP feature extraction. Finally, classification is performed using SVM. Experimental results demonstrate that, on BCI Competition IV datasets 1 and 2a, the proposed method achieves an average binary classification accuracy of 86.95% and 81.48%, respectively, outperforming traditional CSP by 7.44% and 9.57%. In the four-class classification task on BCI Competition IV dataset 2a, it reaches 74.23%, representing a 14.10% improvement over traditional CSP. These findings indicate the effectiveness of the proposed method in MI-EEG classification.