Driving　style　may　have　an　important　effect　on　traffic　safety.　Proactive　crash　risk　prediction　for　lane-changing　behaviors　incorporating　individual　driving　styles　can　help　drivers　make　safe　lane-changing　decisions.　Howev-er,　the　interaction　between　driving　styles　and　lane-changing　risk　is　still　not　fully　understood,　making　it　difficult　for　advanced　driver-assistance　systems　(ADASs)　to　provide　personalized　lane-changing　risk　information　services.　This　paper　proposes　a　personalized　risk　lane-changing　prediction　framework　that　considers　driving　style.　Several　driving　volatility　indices　based　on　vehicle　interactive　features　have　been　proposed,　and　a　dynamic　clustering　method　is　developed　to　determine　the　best　identification　time　window　and　methods　of　driving　style.　The　Light　Gradient　Boosting　Machine　(LightGBM)　based　on　Shapley　additive　explanation　is　used　to　predict　lane-changing　risk　for　cautious,　normal,　and　aggressive　drivers　and　to　analyze　their　risk　factors.　The　highD　trajectory　dataset　is　used　to　evaluate　the　proposed　framework.　The　obtained　results　show　that　i)　spectral　clustering　and　a　time　window　of　3　s　can　accurately　identify　driving　styles　during　the　lane-changing　intention　process;　ii)　the　LightGBM　algorithm　outperforms　other　machine　learning　methods　in　personalized　lane-changing　risk　prediction;　iii)　aggressive　drivers　seek　more　individual　driving　freedom　than　cautious　and　normal　drivers　and　tend　to　ignore　the　state　of　the　car　behind　them　in　the　target　lane,　with　a　greater　lane-changing　risk.　The　research　conclusion　can　provide　basic　support　for　the　development　and　application　of　personalized　lane-changing　warning　systems　in　ADASs.