In the classification and recognition of motor imagery EEG features for limb movements, there exists a problem of low action recognition accuracy when fusing features from different domains. To address this issue, this study designs an EEG-symmetric positive definite network model for motor feature classification, tailored to the complex cross-domain relationships of motor imagery EEG features in multi-channel data collection. This model effectively extracts and integrates features from different domains, achieving accurate classification of limb features and action recognition based on EEG signals. Experimental results demonstrate that on the BCI Competition IV 2a dataset, which contains motor imagery data of four types of limb movements, the proposed classification model achieves an action recognition accuracy of 0.85 and a Kappa coefficient of 0.80, indicating high precision.