Identifying　influencing　factors　of　heavy　metals　is　essential　for　soil　evaluation　and　protection.　This　study　investigates　the　use　of　a　geographical　detector　to　identify　influencing　factors　of　agricultural　soil　heavy　metals　from　natural　and　anthropogenic　aspects.　We　focused　on　six　variables　of　soil　heavy　metals,　i.e.,　As,　Cd,　Hg,　Cu,　Pb,　Zn,　and　four　influencing　factors,　i.e.,　soil　properties　(soil　type　and　soil　texture),　digital　elevation　model　(DEM),　land　use,　and　annual　deposition　fluxes.　Experiments　were　conducted　in　Shunyi　District,　China.　We　studied　the　spatial　correlations　between　variables　of　soil　heavy　metals　and　influencing　factors　at　both　single-object　and　multi-object　levels.　A　geographical　detector　was　directly　used　at　the　single-object　level,　while　principal　component　analysis　(PCA)　and　geographical　detector　were　sequentially　integrated　at　the　multi-object　level　to　identify　influencing　factors　of　heavy　metals.　Results　showed　that　the　concentrations　of　Cd,　Cu,　and　Zn　were　mainly　influenced　by　DEM　(p　=　0.008)　and　land　use　(p　=　0.033)　factors,　while　annual　deposition　fluxes　were　the　main　factors　of　the　concentrations　of　Hg,　Cd,　and　Pb　(p　=　0.000).　Moreover,　the　concentration　of　As　was　primarily　influenced　by　soil　properties　(p　=　0.026),　DEM　(p　=　0.000),　and　annual　deposition　flux　(p　=　0.000).　The　multi-object　identification　results　between　heavy　metals　and　influencing　factors　included　single　object　identification　in　this　study.　Compared　with　the　results　using　the　PCA　and　correlation　analysis　(CA)　methods,　the　identification　method　developed　at　different　levels　can　identify　much　more　influencing　factors　of　heavy　metals.　Due　to　its　promising　performance,　identification　at　different　levels　can　be　widely　employed　for　soil　protection　and　pollution　restoration.