Objective:　The　RSVP　(Rapid　Serial　Visual　Presentation)　paradigm　facilitates　target　identification　in　a　rapid　picture　stream,　which　is　applied　extensively　in　military　target　surveillance　and　police　monitoring.　Most　researchers　concentrate　on　the　single　target　RSVP-BCI　whereas　the　study　of　dual-target　is　scarcely　conducted,　limiting　RSVP　application　considerably.　Methods:　This　paper　proposed　a　novel　classification　model　named　Common　Representation　Extraction-Targeted　Stacked　Convolutional　Autoencoder　(CRE-TSCAE)　to　detect　two　targets　with　one　nontarget　in　RSVP　tasks.　CRE　generated　a　common　representation　for　each　target　class　to　reduce　variability　from　different　trials　of　the　same　class　and　distinguish　the　difference　between　two　targets　better.　TSCAE　aimed　to　control　uncertainty　in　the　training　process　while　requiring　less　target　training　data.　The　model　learned　a　compact　and　discriminative　feature　through　the　training　from　several　learning　tasks　so　as　to　distinguish　each　class　effectively.　Results:　It　was　validated　on　the　World　Robot　Contest　2021　and　2022　ERP　datasets.　Experimental　results　showed　that　CRE-TSCAE　outperformed　the　state-of-the-art　RSVP　decoding　algorithms　and　the　Average　ACC　was　71.25%,　improving　6.5%　at　least　over　the　rest.　Conclusion:　It　demonstrated　that　CRE-TSCAE　showed　a　strong　ability　to　extract　discriminative　latent　features　in　detecting　the　differences　among　two　targets　with　nontarget,　which　guaranteed　increased　classification　accuracy.　Significance:　CRE-TSCAE　provided　an　innovative　and　effective　classification　model　for　dual-target　RSVP-BCI　tasks　and　some　insights　into　the　neurophysiological　distinction　between　different　targets.　IEEE