This　research　is　to　explore　the　relationship　between　a　driver＇s　visual　features　and　driving　behaviors　of　distracted　driving,　and　a　random　forest　(RF)　method　is　developed　to　classify　driving　behaviors　and　improve　the　accuracy　of　detecting　distracted　driving.　Drivers　were　required　to　complete　four　distraction　tasks　while　they　followed　simulated　vehicles　in　the　experiment.　In　data　analysis,　the　features　of　distracted　driving　behaviors　are　first　described,　and　the　visual　data　are　classified　into　three　distraction　levels　based　on　the　AttenD　algorithm.　Based　on　the　collected　data,　this　paper　shows　the　relationship　between　visual　features　and　driving　behavior.　Significant　differences　are　discovered　between　different　distraction　tasks　and　distraction　levels.　Additionally,　driving　behavior　data　is　used　to　build　an　RF　model　to　classify　distracted　driving　into　three　levels.　Results　demonstrate　that　this　model　is　feasible　to　capture　the　classification　of　distraction　and　its　accuracy　for　each　distraction　task　is　over　90%.　Areas　under　receiver　operating　characteristic　curve　calculated　through　error-correcting　output　codes　are　mainly　around　0.9,　indicating　good　reliability.　With　this　classification　method,　distraction　levels　could　be　classified　with　vehicle　operation　characteristics.　The　model　established　by　this　method　could　detect　distractions　in　actual　driving　through　the　detection　of　driving　behavior　without　the　need　of　eye　tracking　systems.