Spring　operation　mechanism　is　widely　used　in　high　voltage　circuit　breakers,　and　its　reliability　is　related　to　the　ability　of　the　circuit　breaker　breaking　fault　current.　During　the　life　cycle　of　spring　operating　mechanism,　stress　relaxation,　metal　fatigue,　and　any　other　mechanical　defects　are　easily　occurring.　And　the　mechanical　performance　of　the　circuit　breaker　will　be　influenced　by　the　above　defects.　Therefore,　identifying　and　predicting　the　mechanical　conditions　of　the　spring　operation　mechanism　can　improve　the　reliability　of　the　circuit　breaker.　In　the　present　paper,　the　252　kV　circuit　breakers　are　used　as　test　objects.　Firstly,　the　spring　stress　relaxation　test,　the　life-cycle　test,　and　the　failure　simulated　test　of　252　kV　circuit　breakers　are　carried　out.　Secondly,　a　multi-body　dynamics　simulation　model　of　the　experimental　prototype　is　established.　Thirdly,　support　vector　machine,　random　forest,　and　deep　neural　network　are　used　in　the　condition　identification　of　the　circuit　breaker　to　compare　their　performances.　Then,　the　prediction　model　of　spring　in　stress　relaxation　test　is　built,　however,　the　model　is　not　suitable　for　the　life-cycle　test　of　repeat　close-open　operation.　Finally,　the　remaining　useful　life　prediction　model　is　proposed　by　using　Wiener　Process.