Accurate emotion recognition is crucial for enhancing human–computer interaction, and brain–computer interface (BCI) technology offers an efficient means for emotion detection using the EEG signal. However, existing methods face significant challenges due to the inherent inter-individual differences and temporal variability of EEG data. To address these limitations, this paper introduces a multi-source domain adaptive algorithm based on dendrite net (DD-MSDA). The proposed model employs the dendrite network as a shared feature extractor to align feature distributions across multiple source domains, thereby capturing common features among diverse datasets. Experimental validation on cross-subject and cross-session tasks using the SEED and SEED-IV datasets demonstrates that DD-MSDA achieves highly competitive performance, outperforming all compared single-modal EEG-based domain adaptation methods. Moreover, the algorithm demonstrates statistically significant advantages over existing domain adaptation baselines in cross-dataset settings. These results highlight the consistent competitiveness of DD-MSDA across various cross-domain scenarios, and its unsupervised nature underscores its potential for practical online EEG emotion recognition applications.