An　active-learning　reliability　method　called　the　AEM-PDEM　is　proposed　that　combines　adaptive　ensemble　of　metamodels　(EM)　and　the　probability　density　evolution　method　(PDEM).　Three　critical　aspects　are　addressed　in　this　method.　First,　the　ensemble　of　three　diverse　metamodels,　i.e.,　the　polynomial　chaos　Kriging　(PCK),　the　low-rank　approximation　(LRA)　and　the　support　vector　regression　(SVR),　is　built　by　weighted　combination　according　to　their　global　error　measures,　which　enables　to　provide　both　predicted　value　and　variance.　Second,　the　EM　predictions　at　the　training　samples　are　replaced　by　the　true　computational　model　responses,　so　as　to　secure　the　accuracy　of　failure　probability　estimate.　Third,　according　to　the　PDEM-oriented　expected　improvement　function　(PEIF),　a　multi-point　enrichment　process　is　developed　based　on　the　EM　and　the　three　component　metamodels.　Then,　three　numerical　examples　are　investigated　and　comparisons　are　made　between　the　AEM-PDEM　and　other　existing　reliability　methods.　Results　demonstrate　that,　in　comparison　with　the　existing　APCK-PDEM,　the　AEM-PDEM　needs　roughly　85-95%　of　the　number　of　computational　model　evaluations.　More　importantly,　it　only　requires　approximately　30-45%　of　the　number　of　iterations　during　the　active-learning　process.　As　a　result,　it　just　consumes　nearly　35-50%　of　computational　time　of　the　APCK-PDEM,　especially　in　high-dimensional　dynamic　problems　and　practical　complex　engineering　problems.　©　2022　Elsevier　Ltd