Driving　style　is　the　external　expression　of　driving　behavior.　Drivers　with　aggressive　style　tend　to　engage　in　more　frequent　risky　driving　operations,　intensifying　interactions　between　vehicles　and　affecting　lane-changing　safety.　Identifying　a　driver’s　driving　style　before　executing　a　lane-changing　can　effectively　constrain　driver’s　behavior　through　personalized　warning　information.　This　paper　proposed　the　SHAP-XGBoost　method,　which　considers　lane-changing　game　in　a　connected　environment,　aiming　to　achieve　the　real-time　recognition　of　driving　styles　during　the　lane-changing　intention　phase.　Firstly,　the　fluctuation　degree　of　individual　behavior　and　gaming　behavior　during　the　lane-changing　intention　was　used　as　input　feature　variables,　and　the　driving　style　was　marked　by　correlation　analysis,　principal　component　analysis,　and　four　different　clustering　methods.　Next,　the　proposed　SHAP-XGBoost　model　was　used　to　select　key　features　for　training　the　driving　style　recognition　model,　and　online　recognition　was　completed　through　a　sliding　window.　Finally,　experiments　were　conducted　using　the　HighD　dataset.　Results　show　that:　compared　with　clustering　methods　based　on　centroid　distance,　connectivity　and　density　distribution,　spectral　clustering　based　on　graph　theory　principles　can　better　label　driving　styles　based　on　the　morphology　of　the　input　feature　variables;　using　the　proposed　SHAP-XGBoost　model　with　14　key　features　for　driving　style　recognition　can　improve　online　recognition　efficiency　without　loss　of　accuracy,　and　the　driving　style　recognition　accuracy　is　up　to　99%;　simultaneously　incorporating　individual　features　and　gaming　features　as　inputs　to　the　model　can　improve　the　accuracy　of　driving　style　labeling　and　recognition.　The　research　results　can　be　used　to　support　personalized　lane-changing　decisions　and　early　warnings.　©　2024　South　China　University　of　Technology.　All　rights　reserved.