Accurately　acquiring　the　ecolevel　of　individual　driver　performance　is　the　precondition　for　more　targeted　ecodriving　behavior　optimization.　Because　of　obvious　advantage　in　mining　hidden　relationship,　machine　learning　was　adopted　to　explore　the　complicated　relationship　between　driver　performance　and　vehicle　fuel　consumption　and　thus　to　predict　the　ecolevel　of　individual　driver　performance　in　this　study.　Based　on　driving　simulator　tests,　data　of　driver　performance　and　vehicle　fuel　consumption　were　collected.　The　ecolevel　was　indicated　as　the　ecoscore　corresponding　to　vehicle　fuel　consumption.　The　model　input　was　designed　as　10　feature　indexes　of　driver　performance　(e.g.,　percentage　number,　mean　value,　standard　deviation,　and　power　of　applying　acceleration　pedal).　The　output　was　treated　as　ecoscore.　Taking　a　number　of　one　hundred　of　data　segments　in　vehicle　starting　process　as　training　sample,　the　optimal　structure,　functions,　and　learning　rate　of　a　backpropagation　neural　network　model　with　three　layers　were　obtained,　after　repeated　model　simulation　experiments.　The　validation　test　of　16　sample　data　items　showed　that　the　mean　prediction　accuracy　of　our　developed　model　was　92.89%.　In　addition,　comparative　analysis　displayed　that　the　performance　of　backpropagation　neural　network　based　model　was　better　than　linear　regression　based　model　and　random　forest　based　model,　from　the　aspects　of　elapsed　time　and　prediction　accuracy　in　estimating　the　ecolevel　of　driver　performance.　The　study　results　provide　an　effective　method　to　grasp　the　ecolevel　of　driver　performance　and　further　contribute　to　driving　behavior　optimization　towards　vehicle　fuel　consumption　and　emissions　reduction.