Quantum　algorithms　can　enhance　machine　learning　in　different　aspects.　The　quantum　support　vector　machine　was　proposed　to　improve　the　performance,　in　which　the　Swap　Test　plays　a　crucial　role　in　realizing　the　classification.　However,　as　the　Swap　Test　is　destructive,　the　quantum　support　vector　machine　must　be　repeated　in　preparing　qubits　and　manipulating　operations.　This　paper　proposes　a　quantum　support　vector　machine　based　on　the　amplitude　estimation　(AE-QSVM)　which　gets　rid　of　the　constraint　of　repetitive　process　and　saves　the　quantum　resources.　At　first,　a　generalized　quantum　amplitude　estimation　is　introduced　in　which　the　initial　state　can　be　arbitrary　instead　of　being　|0〉.　Then,　AE-QSVM　is　trained　by　the　quantum　singular　value　decomposition　and　a　query　sample　is　classified　by　the　generalized　quantum　amplitude　estimation.　In　AE-QSVM,　a　high　accuracy　can　be　achieved　by　adding　auxiliary　qubits　instead　of　repeating　the　algorithm.　The　time　and　space　complexity　of　AE-QSVM　are　reduced　compared　with　other　algorithms.　Finally,　we　ran　experiments　on　the　IBM＇s　quantum　computer　and　experimental　results　demonstrate　that　classification　with　a　95%　probability　of　success　only　uses　12　qubits.　©　2023　Elsevier　Inc.