The　tunnel　is　an　important　component　of　freeway　operation　safety,　and　its　classification　method　is　the　foundation　of　a　refined　management　of　operation　safety.　At　present,　the　impact　of　different　categories　of　tunnels　on　driver　safety,　comfort,　and　driving　behavior　under　naturalistic　driving　conditions　is　not　clear,　and　there　is　a　lack　of　classification　methods　for　tunnels　of　different　lengths　in　their　operation　stages.　This　paper　was　based　on　the　driving　workload,　which　effectively　expresses　the　safety　and　comfort　of　drivers.　In　this　context,　naturalistic　driving　experiments　in　13　freeways　and　98　tunnels　with　36　participants　were　carried　out.　The　DDTW+K-Means++　algorithm,　which　is　suitable　for　drivers＇　driving　workload　time　series　data,　was　used　for　a　clustering　analysis　of　the　tunnels.　According　to　the　length　of　the　tunnel,　the　operation-stage　tunnels　were　divided　into　three　categories:　short　tunnels　(＜450　m),　general　tunnels　(450　similar　to　4000　m),　and　long　tunnels　(＞4000　m).　The　length　of　the　tunnel　had　a　positive　correlation　with　the　drivers＇　driving　workload,　while　there　was　a　negative　correlation　with　the　vehicle　running　speed,　and　the　range　of　changes　in　the　drivers＇　driving　workload　and　operation　safety　risks　in　general　tunnels　and　long　tunnels　was　higher　than　that　in　short　tunnels.　Road　and　environmental　conditions　are　important　factors　affecting　the　driving　workload.　The　entrance　area,　the　exit　area　of　tunnels,　and　the　middle　area　of　long　tunnels　are　high-risk　sections　in　the　affected　area　of　the　tunnel.　These　research　results　are　of　great　significance　for　the　operation　safety　management　of　freeway　tunnels.