In　recent　years,　the　active　learning　reliability　method　that　combines　the　Kriging　model　and　Monte　Carlo　simulation　(AK-MCS)　has　emerged　as　a　promising　approach　due　to　its　computational　efficiency　and　accuracy.　However,　the　commonly　used　learning　functions,　such　as　the　expected　feasibility　function　(EFF),　U　function,　H　function,　and　expected　risk　function　(ERF),　can　only　select　one　training　point　at　each　iteration　which　is　timewasteful　when　parallel　computing　is　available.　Therefore,　this　paper　proposes　a　parallel　active　learning　Kriging　strategy,　namely　P-AK-MCS,　for　structural　reliability　analysis.　By　introducing　an　influence　function　that　reflects　the　impact　of　the　added　point　on　the　original　learning　function,　four　parallel　learning　functions　are　constructed:　pseudo-U　(PU)　function,　pseudo-EFF　(PEFF),　pseudo-H　(PH)　function,　and　pseudo-ERF　(PERF).　These　functions　aim　to　identify　multiple　training　points　at　each　iteration　without　requiring　additional　functional　evaluations.　The　effectiveness　of　the　proposed　method　is　validated　using　four　examples.　The　results　demonstrate　that　compared　to　the　standard　AK-MCS,　the　proposed　P-AK-MCS　significantly　reduces　the　number　of　computation　loops　and　greatly　decreases　computational　costs.　Moreover,　the　total　number　of　functional　evaluations　required　is　similar　to　that　of　the　standard　AK-MCS　and　remains　insensitive　to　the　number　of　multiple　training　points.