Based　on　combining　the　relevant　studies　on　situation　awareness　(SA),　this　paper　integrated　multiple　indicators,　including　eye　movement,　electroencephalogram　(EEG),　and　driving　behavior,　to　evaluate　SA.　SA　is　typically　divided　into　three　stages:　perception,　understanding　and　prediction.　This　paper　used　eye　movement　indicators　to　represent　perception,　EEG　indicators　to　represent　understanding,　and　driving　behavior　indicators　to　represent　prediction.　After　identifying　indicators　for　evaluating　SA,　a　driving　simulation　experiment　was　designed　to　collect　data　on　the　indicators.　41　subjects　were　recruited　to　participate　in　the　investigation,　and　the　experimenter　collected　data　from　each　subject　in　a　total　of　9　groups.　After　removing　4　groups　of　invalid　data,　365　groups　of　valid　data　were　finally　obtained.　The　grey　correlation　analysis　was　used　to　optimize　the　SA　indicators,　and　10　SA　evaluation　indicators　were　finally　determined.　There　were　the　average　fixation　duration,　the　nearest　neighbor　index,　pupil　area,　the　percentage　power　spectral　density　values　of　the　3　rhythmic　waves　(0　,　alpha　,　beta),　rhythmic　wave　energy　combination　parameters　(　alpha　/　0),　mean　speed,　SD　of　speed　and　acceleration.　Taking　the　optimized　10　indicators　as　input　and　the　SA　scores　as　output,　a　backpropagation　neural　network　model　with　a　topological　structure　of　10-8-1　was　constructed.　75%　of　the　data　were　randomly　selected　for　model　training,　and　the　final　network　training　＇s　mean　square　error　was　0.0025.　Using　the　remaining　25%　of　data　for　verification,　the　average　absolute　error　and　average　relative　error　of　the　predicted　results　are　0.248　and　0.046,　respectively.　This　showed　that　the　model　was　effective,　and　it　was　feasible　to　evaluate　the　SA　by　using　the　data　of　eye　movement,　EEG　and　driving　behavior　parameters.