In　order　to　improve　the　prediction　accuracy　of　performance　degradation　trends　of　rolling　bearings,　a　method　based　on　the　joint　approximative　diagonalization　of　eigen-matrices　(JADE)　and　particle　swarm　optimization　support　vector　machine　(PSO-SVM)　was　proposed.　Firstly,　the　features　of　the　time-domain,　frequency-domain,　and　time-frequency-domain　eigenvalues　of　the　vibration　signal　corresponding　to　the　entire　life　cycle　of　the　rolling　bearing　are　extracted,　and　the　performance　degradation　parameters　are　initially　selected　by　using　the　monotonicity　parameter.　Then,　a　fusion　feature　that　can　effectively　represent　the　performance　degradation　is　obtained　by　using　the　JADE　method.　Finally,　the　prediction　model　based　on　PSO-SVM　is　constructed　to　predict　the　performance　degradation　trend.　By　comparing　with　the　prediction　results　obtained　by　other　classical　methods,　it　can　be　proved　that　this　method　can　accurately　predict　the　performance　degradation　trend　and　the　remaining　useful　life　(RUL)　of　rolling　bearings　under　small　sample　sizes,　and　has　considerable　application　potentials.