As　a　crucial　component　supporting　aero-engine　functionality,　effective　fault　diagnosis　of　bearings　is　essential　to　ensure　the　engine　＇　s　reliability　and　sustained　airworthiness.　However,　practical　limitations　prevail　due　to　the　scarcity　of　aero-engine　bearing　fault　data,　hampering　the　implementation　of　intelligent　diagnosis　techniques.　This　paper　presents　a　specialized　method　for　aero-engine　bearing　fault　diagnosis　under　conditions　of　limited　sample　availability.　Initially,　the　proposed　method　employs　the　refined　composite　multiscale　phase　entropy　(RCMPhE)　to　extract　entropy　features　capable　of　characterizing　the　transient　signal　dynamics　of　aero-engine　bearings.　Based　on　the　signal　amplitude　information,　the　composite　multiscale　decomposition　sequence　is　formulated,　followed　by　the　creation　of　scatter　diagrams　for　each　sub-sequence.　These　diagrams　are　partitioned　into　segments,　enabling　individualized　probability　distribution　computation　within　each　sector,　culminating　in　refined　entropy　value　operations.　Thus,　the　RCMPhE　addresses　issues　prevalent　in　existing　entropy　theories　such　as　deviation　and　instability.　Subsequently,　the　bonobo　optimization　support　vector　machine　is　introduced　to　establish　a　mapping　correlation　between　entropy　domain　features　and　fault　types,　enhancing　its　fault　identification　capabilities　in　aero-engine　bearings.　Experimental　validation　conducted　on　drivetrain　system　bearing　data,　actual　aero-engine　bearing　data,　and　actual　aerospace　bearing　data　demonstrate　remarkable　fault　diagnosis　accuracy　rates　of　99.83　%,　100　%,　and　100　%,　respectively,　with　merely　5　training　samples　per　state.　Additionally,　when　compared　to　the　existing　eight　fault　diagnosis　methods,　the　proposed　method　demonstrates　an　enhanced　recognition　accuracy　by　up　to　28.97　%.　This　substantiates　its　effectiveness　and　potential　in　addressing　small　sample　limitations　in　aero-engine　bearing　fault　diagnosis.