Frequent　acceleration　and　deceleration　behavior　and　the　var-ious　merging　speed　of　different　vehicles　have　caused　the　merging　area　to　become　a　high-risk　section.　To　investigate　the　influencing　factors　and　the　underlying　relationships　on　the　traffic　conflicts,　an　in-depth　analysis　of　traffic　conflicts　at　merging　areas　is　conducted.　UAV　video　data　is　used　to　extract　vehicle　trajectories　to　collect　microscopic　influencing　factors.　A　Bayesian　network　model　is　proposed　to　explore　the　in-ner-relationship　among　influencing　factors.　An　infor-mation-entropy　and　decision-tree　based　discretization　method　(IEDT)　is　used　to　improve　the　performance　of　the　Bayesian　network　model.　Key　traffic　conflict　chain　are　then　identified　based　on　the　Bayesian　network.　The　results　show　that　IEDT　can　improve　the　prediction　accuracy　of　Bayesian　network　model　about　9%　than　K-means,　and　24%　than　equal　distance　method.　Unstable　lane-keeping　ability,　too　small　or　too　large　merging　duration　etc.　are　significant　factors　that　will　increase　the　merging　conflicts.　The　speed　of　on-ramp　vehicle　is　the　key　factor　that　have　inner-relationship　with　other　factors.　These　results　provide　further　insight　to　the　mechanism　of　merging　traffic　conflicts,　and　can　be　helpful　for　traffic　engineers　and　traffic　enforcement　officers　take　countermeasures　to　improve　traffic　safety.　　©　2022　IEEE.