Accurate　and　proactive　lane-changing　(LC)　intention　recognition　can　assist　drivers　in　making　LC　decisions　to　improve　driving　safety.　However,　the　mechanism　of　drivers’　LC　decisions　in　dynamically　changing　environments　is　still　not　fully　understood,　which　makes　it　difficult　for　advanced　driver-assistance　systems　(ADASs)　to　make　accurate　LC　decisions　under　different　working　conditions.　To　accurately　capture　the　dynamic　features　before　the　generation　of　LC　intention,　relying　on　the　multivehicle　interaction　capability　of　the　connected　environment,　a　LC　intention　recognition　framework　using　graph　theory　to　model　the　interaction　relationship　among　multiple　vehicles,　i.e.,　the　Multivehicle　Interaction　Dynamic　Time　Graph　(MIDTG)　framework,　is　proposed.　First,　the　interaction　relationship　between　LC　vehicles　and　their　surrounding　vehicles　in　the　connected　communication　range　is　modeled　by　graph　theory.　Second,　the　graph　convolutional　network　(GCN)　is　used　to　extract　spatial　features　of　multivehicle　interactions,　and　a　long　short-term　memory　(LSTM)　neural　network　is　used　to　learn　the　association　of　multivehicle　interaction　graphs　in　a　time　series.　Finally,　LC　intentions　are　output　through　the　Softmax　function.　The　highD　data　set　is　used　to　validate　the　proposed　model.　Results　show　that　the　model　can　accurately　extract　the　dynamic　features　of　multivehicle　interactions　within　a　time　window　of　2.5　to　3.5　s,　and　the　accuracy　of　LC　intention　recognition　reaches　98%,　which　is　an　average　improvement　of　3.5%　compared　with　other　baseline　models.　The　study　provides　a　new　way　to　model　multivehicle　interactions　in　the　connected　environment,　which　can　be　helpful　for　ADASs’　LC　decision-making.　©　2024　American　Society　of　Civil　Engineers.