The　rapid　serial　visual　presentation-based　brain-computer　interface　(RSVP-BCI)　system　achieves　the　recognition　of　target　images　by　extracting　event-related　potential　(ERP)　features　from　electroencephalogram　(EEG)　signals　and　then　building　target　classification　models.　Currently,　how　to　reduce　the　training　and　calibration　time　for　classification　models　across　different　subjects　is　a　crucial　issue　in　the　practical　application　of　RSVP.　To　address　this　issue,　a　zero-calibration　(ZC)　method　termed　Attention-ProNet,　which　involves　meta-learning　with　a　prototype　network　integrating　multiple　attention　mechanisms,　was　proposed　in　this　study.　In　particular,　multiscale　attention　mechanisms　were　used　for　efficient　EEG　feature　extraction.　Furthermore,　a　hybrid　attention　mechanism　was　introduced　to　enhance　model　generalization,　and　attempts　were　made　to　incorporate　suitable　data　augmentation　and　channel　selection　methods　to　develop　an　innovative　and　high-performance　ZC　RSVP-BCI　decoding　model　algorithm.　The　experimental　results　demonstrated　that　our　method　achieved　a　balance　accuracy　(BA)　of　86.33%　in　the　decoding　task　for　new　subjects.　Moreover,　appropriate　channel　selection　and　data　augmentation　methods　further　enhanced　the　performance　of　the　network　by　affording　an　additional　2.3%　increase　in　BA.　The　model　generated　by　the　meta-learning　prototype　network　Attention-ProNet,　which　incorporates　multiple　attention　mechanisms,　allows　for　the　efficient　and　accurate　decoding　of　new　subjects　without　the　need　for　recalibration　or　retraining.