Addressing　the　efficiency　and　accuracy　issues　of　radio　frequency　fingerprint　recognition　in　laboratory　access　control,　this　paper　presents　a　recognition　model　based　on　model-agnostic　meta-learning　and　radio　frequency　fingerprint.　By　integrating　short-time　Fourier　transform　and　constellation　diagram,　the　proposed　approach　significantly　enhances　identification　performance.　The　experimental　results　show　that　in　the　simulation　run　experiment,　the　average　recognition　accuracy　of　the　proposed　model　before　and　after　training　is　0.59　and　0.97,　respectively.　When　the　training　sample　size　is　10　and　5,　the　average　recognition　efficiency　of　the　proposed　model　is　93.80%　and　90.15%,　respectively.　When　the　model　adds　the　short-time　Fourier　transform　and　constellation　diagram,　its　recognition　accuracy　and　efficiency　increase　by　0.19　and　13.01%,　respectively.　In　addition,　in　actual　model　performance　experiments,　the　proposed　model　shows　the　most　stable　performance,　with　an　average　of　90.47%　for　recognition　accuracy　and　0.91　for　efficiency.　Moreover,　the　recognition　accuracy　of　the　model　for　different　types　of　signals　can　reach　up　to　0.91.　In　environments　with　signal-to-noise　ratios　of　5　and　0,　the　average　recognition　efficiency　of　the　proposed　model　is　83.20%　and　62.00%,　respectively.　The　original　contributions　of　the　study　lie　in　the　optimization　of　feature　extraction　and　model　training　processes,　which　significantly　enhance　the　accuracy　and　efficiency　of　radio　frequency　fingerprint　recognition,　especially　in　scenarios　with　limited　sample　sizes.　This　addresses　the　issues　of　low　accuracy　and　poor　efficiency　in　traditional　radio　frequency　fingerprint　models.　The　study　is　capable　of　improving　the　efficiency　and　accuracy　of　radio　frequency　fingerprint　recognition　and　enhancing　the　level　of　laboratory　safety　management　and　control.　©　The　Author(s)　2025.;　Efficient　Recognition:　New　model　uses　advanced　machine　learning　to　boost　accuracy　and　efficiency　with　limited　data.　Innovative　Methods:　Enhanced　feature　extraction　and　model　training　improve　adaptability　for　better　security.　Practical　Impact:　The　model　enhances　lab　access　control　efficiency,　offering　insights　for　intelligent　management.　©　The　Author(s)　2025.