The　subjectivity　of　selecting　training　parameters　is　an　important　factor　affecting　the　accuracy　of　short-term　passenger　flow　prediction　of　rail　transit　by　long　short-term　memory　(LSTM)　neural　network.　In　order　to　improve　the　prediction　accuracy,　an　improved　particle　swarm　optimization　(IPSO)　algorithm　is　proposed　to　optimize　the　LSTM.　The　size　of　the　learning　factor　of　the　particle　swarm　optimization　(PSO)　algorithm　is　controlled　by　dynamic　adjustment　method　to　improve　the　global　optimization　and　convergence　ability　of　the　algorithm.　The　number　of　hidden　layer　nodes,　learning　rate　and　iteration　times　of　the　LSTM　are　optimized　by　IPSO.　The　passenger　flow　data　of　Dongjiekou　station　of　Fuzhou　Metro　Line　1　are　selected　for　verification,　and　the　proposed　model　is　compared　with　the　traditional　prediction　model.　The　results　show　that　the　LSTM　optimized　by　the　improved　particle　swarm　optimization　algorithm　can　effectively　predict　the　short-term　passenger　flow　of　rail　transit.　Compared　with　the　PSO-LSTM　model,　the　root　mean　square　error　predicted　by　the　IPSO-LSTM　model　decreases　by　10.87%　in　the　peak　period　and　by　26%　in　the　off-peak　period.　The　results　can　provide　theoretical　and　technical　support　for　the　optimization　of　rail　transit　operation　scheme.