Aiming　at　the　difficulty　of　features　extraction　of　rotating　mechanical　equipments,　a　fault　diagnosis　method　based　on　the　entropy-manifold　feature　and　salp　swarm　optimization　support　vector　machine　(SSO-SVM)　was　proposed.　First,　the　improved　multiscale　weighted　permutation　entropy　(IMWPE)　was　utilized　to　extract　fault　features　of　the　mechanical　equipment　under　different　working　conditions.　Then,　the　S-Isomap,　as　a　manifold　learning　method,　was　employed　to　reduce　the　dimension,　and　obtain　the　low-dimensional　entropy-manifold　feature　set.　Finally,　the　entropy-manifold　features　were　input　into　a　SSO-SVM　multi-fault　classifier　for　identification　and　diagnosis.　The　experimental　results　of　the　planetary　gearbox　fault　diagnosis　show　that　the　IMWPE+S-Isomap　entropy-manifold　feature　extraction　method　is　superior　to　the　existing　entropy-based　feature　extraction　methods　of　multiscale　permutation　entropy　(MPE),　multiscale　weighted　permutation　entropy　(MWPE)　and　IMWPE.　It　is　also　more　advantageous　than　the　existing　entropy-manifold　feature　extraction　methods　of　IMWPE+isometric　mapping　(Isomap)　and　IMWPE+linear　local　tangent　space　alignment　(LLTSA).　The　salp　swarm　algorithm　is　better　than　the　particle　swarm　algorithm,　gray　wolf　algorithm,　artificial　bee　colony　algorithm　and　bat　algorithm　for　the　optimization　of　support　vector　machine　parameters.　The　proposed　fault　diagnosis　method　has　a　diagnostic　accuracy　of　100%,　which　can　effectively　examine　the　types　of　working　conditions　of　planetary　gearboxes.　©　2021,　Editorial　Office　of　Journal　of　Vibration　and　Shock.　All　right　reserved.