The　fault　characteristic　signals　of　rolling　bearings　are　coupled　with　each　other,　thus　increasing　the　difficulty　in　identifying　the　fault　characteristics.　In　this　study,　a　fault　diagnosis　method　of　rolling　bearing　based　on　least　squares　support　vector　machine　is　proposed.　First,　least　squares　support　vector　machine　model　is　trained　with　the　samples　of　known　classes.　Least　squares　support　vector　machine　algorithm　involves　the　selection　of　a　kernel　function.　The　complexity　of　samples　in　high-dimensional　space　can　be　adjusted　through　changing　the　parameters　of　kernel　function,　thus　affecting　the　search　for　the　optimal　function　as　well　as　final　classification　results.　Particle　swarm　optimization　and　10-fold　cross-validation　method　are　adopted　to　optimize　the　parameters　in　the　training　model.　Then,　with　the　optimized　parameters,　the　classification　model　is　updated.　Finally,　with　the　feature　vector　of　the　test　samples　as　the　input　of　least　squares　support　vector　machine,　the　pattern　recognition　of　the　testing　samples　is　performed　to　achieve　the　purpose　of　fault　diagnosis.　The　actual　bearing　fault　data　are　analyzed　with　the　diagnosis　method.　This　method　allows　the　accurate　classification　results　and　fast　diagnosis　and　can　be　applied　in　the　diagnosis　of　compound　faults　of　rolling　bearing.