Lane　change　identification　based　on　long　short-term　memory　(LSTM)　neural　networks　has　received　increasing　attention.　The　most　commonly　known　disadvantage　of　the　approach　is　the　＂black　box　＂　nature.　This　study,　to　improve　the　interpretability,　proposed　a　novel　method　for　identifying　lane　changes.　The　philosophy　was　to　measure　the　similarity　of　the　glance　behavior　during　the　maneuver.　The　glance　behavior　in　the　same　maneuver　is　more　similar,　while　that　in　different　maneuvers　is　different.　First,　a　driving　simulator　was　used　to　collect　driving　behavior　data.　The　eye　gaze　time　series　were　captured　by　the　eye　tracker.　The　median　of　the　eye　gaze　sequence　of　3　s　before　the　initial　moment　of　lane　change　was　obtained.　With　the　median　as　the　center,　the　driver＇s　visual　field　plane　was　divided　into　several　grids　with　the　side　length　of　600　pixels.　A　sliding　space-time　cuboid　algorithm　was　proposed　to　extract　the　scanning　path.　Second,　four-dimensional　dynamic　time　warping　(4DDTW)　distance　was　used　to　measure　the　similarity　of　the　scanning　paths.　Third,　the　K　nearest　neighbor　(KNN)　algorithm　was　used　to　classify　the　driving　maneuver　into　the　lane-keeping　(LK),　the　right　lane　change　(RLC),　and　the　left　lane　change　(LLC)　based　on　the　4DDTW　distance　of　the　scan　paths.　LSTM　was　also　used　to　classify　the　driving　maneuver　into　LK,　RLC,　and　LLC.　The　performances　of　4DDTW-KNN　and　LSTM　were　compared.　The　accuracies　of　4DDTW-KNN　and　LSTM　were　86.50%　and　86.33%,　respectively.　LSTM　is　not　better　than　4DDTW-KNN　in　lane　change　identification　based　on　eye　gaze　data　with　a　comprehensive　consideration　of　the　time　efficiency,　the　accuracy　and　the　interpretability.