Entropy　theories　play　a　significant　role　in　rotating　machinery　fault　detection.　The　key　parameters　of　these　methods　are,　however,　often　selected　subjectively　based　on　trial-and-error　methods　or　engineering　experience.　Unsuitable　parameters　would　result　in　an　inconsistency　between　the　extracted　entropy　results　and　the　realistic　case.　In　order　to　address　this　issue,　a　complexity　measurement　method　called　＂swarm　intelligence　optimization　entropy＂　(SIOE)　is　proposed,　which　adaptively　estimates　optimal　parameters　using　skewness　metrics,　logistic　chaos　theory,　and　African　vulture　optimization　(AVO).　By　considering　the　variability　and　dynamic　changes　of　various　signals,　SIOE　enables　the　extraction　of　robust　and　discriminative　dynamic　features.　Additionally,　a　collaborative　intelligent　fault　detection　method　for　rotating　machinery　fault　detection　is　developed,　based　on　SIOE　and　extreme　gradient　boosting　(XGBoost).　This　method　aims　to　accurately　identify　single　faults,　compound　faults,　and　varying　fault　degrees　within　the　rotating　machinery.　Simulation　and　fault　detection　experiments　on　rotating　machines　demonstrate　that　SIOE　improves　recognition　accuracy　by　up　to　21.25%　compared　to　existing　entropy　methods.　The　proposed　intelligent　fault　detection　method　improves　recognition　accuracy　by　up　to　15.71%　compared　to　advanced　fault　detection　methods.　These　results　highlight　the　advantages　of　SIOE　in　complexity　measurement　and　feature　extraction,　as　well　as　the　effectiveness　and　accuracy　of　the　proposed　intelligent　fault　detection　method,　in　identifying　rotating　machinery　faults.