Software　vulnerabilities　will　make　the　system　vulnerable　to　attack,　affect　the　reliability　of　the　software　and　cause　information　leakage,　which　will　have　a　huge　impact　on　enterprises　or　individuals.　Vulnerabilities　are　inevitable　in　software　development　engineering.　Therefore,　relying　on　some　methods　or　tools　for　continuous　vulnerability　analysis　of　code　is　the　solution　to　minimize　software　vulnerabilities.　We　propose　a　neural　network　model　-　JSVulExplorer　for　static　vulnerability　analysis　of　the　dynamic　programming　language　JavaScript.　The　JSVulExplorer　focuses　on　feature　enhancement　of　data.　We　use　pre-training　to　learn　the　semantic　similarity　between　code　slices,　utilize　abstract　syntax　trees　to　generate　path　information,　and　design　positional　encoding　to　use　the　path　information.　Based　on　transfer　learning,　we　combine　the　pre-trained　model　with　path　information　to　improve　vulnerability　detection　performance.　Experiments　show　that　JSVulExplorer　has　significantly　improved　precision　and　recall　compared　to　previous　models.　It　is　verified　that　the　dynamic　event-based　programming　language　can　also　use　the　static　analysis　method　for　vulnerability　detection.　©　2023　SPIE.