As　electric　bikes　(e-bikes)　rapidly　develop　in　China,　their　traffic　safety　issues　are　becoming　increasingly　prominent.　Accurately　detecting　risky　riding　behaviors　and　conducting　mechanism　analysis　on　the　multiple　risk　factors　are　crucial　in　formulating　and　implementing　precise　management　policies.　The　emergence　of　shared　ebikes　and　the　advancements　in　interpretable　machine　learning　present　new　opportunities　for　accurately　analyzing　the　determinants　of　risky　riding　behaviors.　The　primary　objective　of　this　study　is　to　examine　and　analyze　the　risk　factors　related　to　speeding　behavior　to　aid　urban　management　agencies　in　crafting　necessary　management　policies.　This　study　utilizes　a　large-scale　dataset　of　shared　e-bike　trajectory　data　to　establish　a　framework　for　detecting　speeding　behavior.　Subsequently,　the　extreme　gradient　boosting　(XGBoost)　model　is　employed　to　identify　the　level　of　speeding　risk　by　leveraging　its　excellent　identification　ability.　Moreover,　based　on　measuring　the　degree　of　interaction　among　road,　traffic,　and　weather　characteristics,　the　investigation　of　the　complex　interactive　effects　of　these　risk　factors　on　high-risk　speeding　is　conducted　using　bivariate　partial　dependence　plots　(PDP)　by　its　superior　parsing　ability.　Feature　importance　analysis　results　indicate　that　the　top　five　ranked　variables　that　significantly　affect　the　identified　results　of　speed　risk　levels　are　land　use　density,　rainfall,　road　level,　curbside　parking　density,　and　bike　lane　width.　The　interaction　analysis　results　indicate　that　higher　levels　of　road　and　bike　lane　width　correspond　to　an　increased　possibility　of　high-risk　speeding　among　riders.　Land　use　density,　curbside　parking　density,　and　rainfall　display　a　nonlinear　effect　on　high-risk　speeding.　Introducing　road　level,　bike　lane　width,　and　time　interval　could　change　the　patterns　of　nonlinear　effects　in　land　use　density,　curbside　parking　density,　and　rainfall.　Finally,　several　policy　recommendations　are　proposed　to　improve　e-bike　traffic　safety　by　utilizing　the　extracted　feature　values　associated　with　a　higher　probability　of　high-　risk　speeding.