The　drift　ratio　or　lateral　deformation　is　typically　applied　as　the　indicator　in　order　to　evaluate　the　earthquakeinduced　damage,　one　of　the　most　important　issues　is　to　determine　the　seismic　performance　level　limits.　Therefore,　this　study　presents　to　predict　the　seismic　performance　level　limits　of　RC　columns　by　using　the　machine　learning　method.　Firstly,　a　test　database　of　the　backbone　curves　of　RC　columns　was　established　after　collecting　754　specimens　under　axial　and　lateral　loads.　Then　the　seismic　performance　level　limits　of　all　the　collected　columns　were　taken　out　as　the　input　values　of　machine　learning.　The　correlations　among　the　geometric,　mechanical　parameters　and　the　performance　limits　of　RC　columns　were　analyzed　based　on　Pearson　correlation　analysis　and　mutual　information　method.　Afterward,　regression　models　of　seven　machine　learning　methods　were　established　to　predict　the　performance　level　limits　of　RC　columns,　while　the　hyperparameters　of　the　machine　learning　models　were　optimized　by　the　grid　search　and　cross-validation　methods.　The　generalization　ability　of　the　prediction　models　was　verified　and　evaluated　by　using　mean　square　error,　mean　absolute　error,　maximum　error　and　R　square,　meanwhile,　the　accuracy　of　the　applied　methods　was　also　analyzed.　The　seismic　performance　level　limits　of　RC　columns　determined　by　the　machine　learning　method　can　comprehensively　consider　the　influence　of　geometric　and　mechanical　parameters　of　RC　columns.　Combined　with　the　earthquake-induced　deformation　of　RC　columns,　the　seismic　damage　of　RC　columns　can　be　evaluated　reasonably,　which　is　of　great　significance　for　evaluating　the　seismic　damage　of　building　structures.　The　discussion　on　the　prediction　accuracy　among　different　machine　learning　algorithms　is　also　beneficial　for　the　deformation　prediction　of　other　RC　components.