The　Rapid　Serial　Visual　Presentation　(RSVP)　is　a　widely　used　paradigm　for　target　detection　tasks　in　Brain-Computer　Interface　(BCI)　by　decoding　Electroencephalogram　(EEG)　signals.　One　major　issue　concerns　the　time-consuming　calibration　in　cross-subject　scenarios,　which　worsens　in　dual-target　RSVP-BCI　tasks.　A　new　method　is　desperately　needed　to　detect　two　targets　further　with　less　calibration　time.　This　paper　proposed　a　novel　framework　named　Cross-subject　Invariant　Representation　Extraction-Targeted　Stacked　Convolutional　Autoencoder　(CS-IRE-TSCAE)　based　on　reconstructing　the　invariant　representation.　After　filtering　the　source　subjects,　the　CS-TSCAE　alleviates　the　subject-dependent　effect　by　reconstructing　the　invariant　representation　generated　by　CS-IRE.　It　was　validated　on　the　ERP　datasets　from　the　BCI　Controlled　Robot　Contest　2022.　The　experimental　result　showed　that　CS-IRE-TSCAE　obtained　the　highest　Recall,　F1　and　Average　ACC　with　significant　differences　both　in　subject-dependent　and　inter-subject　experiments.　It　demonstrated　that　CS-IRE-TSCAE　achieved　a　higher　classification　performance　for　dual-target　RSVP　with　less　calibration　time.　Our　framework　drives　the　application　development　of　target　detection　in　RSVP-BCI　by　facilitating　multiple　target　detection　in　cross-subject　scenarios,　which　has　practical　significance,　especially　in　fast-deployment　scenarios.　©　2024　Elsevier　B.V.