Vehicles　＇　lane-changing　behavior　can　potentially　result　in　traffic　conflicts　and　crash　risks,　particularly　in　scenarios　with　interactions　among　multiple　vehicles.　To　assess　the　crash　risk　of　multi-vehicle　interaction　lane-changing　(MILC)　scenarios,　this　study　presents　a　dynamic　traffic　graph-based　risk　assessment　method.　First,　a　method　for　constructing　dynamic　scene　graphs　is　proposed,　along　with　graph-based　indicators　for　assessing　scene　and　scenario　risks.　Second,　the　Gaussian　mixture　model-latent　Dirichlet　allocation　(GMM-LDA)　algorithm　is　utilized　to　cluster　risk　sequences　of　MILC　scenarios,　enabling　the　classification　of　scenario　risks　into　different　levels.　The　method　considers　both　dynamic　and　static　elements　in　these　scenarios,　as　well　as　the　spatial　relationships　among　these　elements.　A　case　study　was　conducted　to　illustrate　this　approach　at　a　weaving　area　in　China.　The　findings　reveal　that　diverging　segments　have　the　highest　probability　of　high-risk　MILC　scenarios.　Furthermore,　the　likelihood　of　high-risk　scenarios　involving　consecutive　lane-changing　maneuvers　is　higher　compared　to　single　lane　changes.　Left　lanechanging　behavior　exhibits　a　higher　probability　of　high-risk　scenarios　compared　to　right　lanechanging.　The　proposed　risk　assessment　method　facilitates　the　identification　and　construction　of　high-risk　MILC　scenarios.　It　also　enables　the　exploration　of　the　risk　evolution　process.