Contamination　of　gasoline　by　wax　deposit　of　diesel　is　a　severe　problem　in　sequential　transportation　of　product　oil　pipelines　in　cold　areas.　However,　most　works　on　wax　deposition　are　focused　on　crude　oil.　In　response,　this　paper　aims　to　investigate　wax　deposition　from　a　unique　perspective　of　diesel　oil　in　sequential　transportation.　To　this　end,　a　cold　finger　apparatus　was　designed　and　constructed.　It　is　found　that　the　wax　deposition　rate　of　diesel　oil　increases　with　oil　temperature　and　wax　content,　and　decreases　with　cold　finger　temperature.　A　non-monotonic　variation　trend　is　observed　against　shear　stress.　To　predict　diesel　wax　deposition　rate,　a　back　propagation　(BP)　neural　network　optimized　by　bald　eagle　search　(BES)　algorithm　is　proposed.　Grey　relational　analysis　(GRA)　is　employed　to　get　the　highly　relevant　factors　as　input　parameters　of　the　developed　model.　Prediction　accuracy　and　generalization　ability　of　the　BES-BP　model　is　experimentally　verified.　This　work　will　be　helpful　to　schedule　the　transportation　program　of　product　oil　to　avoid　contamination　of　gasoline　by　diesel　wax　deposit.