Electroencephalogram　(EEG)-based　emotion　recognition　has　become　a　focus　of　brain–computer　interface　research.　However,　differences　in　EEG　signals　across　subjects　can　lead　to　poor　generalization.　Moreover,　current　approaches　individually　extract　temporal　and　spatial　information,　resulting　in　inadequate　feature　fusion　during　feature　extraction.　This　study　develops　a　novel　ChannelMix-based　transformer　and　convolutional　multi-view　feature　fusion　network　(CMTCF)　to　enhance　cross-subject　EEG　emotion　recognition.　Specifically,　a　bi-directional　fusion　module　based　on　a　convolutional　neural　network　(CNN)-Transformer　structure　is　introduced　to　extract　multi-view　spatial　feature　and　temporal　feature,　enabling　the　representation　of　rich　spatiotemporal　information.　Subsequently,　the　ChannelMix　module　is　designed　to　effectively　establish　an　intermediate　domain,　facilitating　the　alignment　of　the　target　and　source　domains　to　reduce　their　discrepancies.　Additionally,　a　soft　pseudo-label　module　is　implemented　to　enhance　the　discriminative　power　of　target　domain　data　within　the　feature　space.　To　further　improve　generalization,　a　ChannelMix-based　data　augmentation　method　is　utilized.　Comprehensive　experiments　are　conducted　on　the　SEED,　SEED-IV　and　SEED-VII　benchmark　datasets,　achieving　recognition　accuracies　of　93.80%　(±4.96),　79.37%　(±6.05)　and　49.13%　(±8.22),　respectively,　demonstrating　that　the　CMTCF　network　achieves　competitive　results　in　cross-subject　EEG　emotion　recognition　tasks.　©　2025　Elsevier　Ltd