The　daily　trajectories　of　individual　movements　convey　a　concise　overview　of　their　behaviors,　with　different　social　roles　having　different　trajectory　patterns.　Therefore,　we　can　identify　users　or　groups　based　on　the　similar　of　their　trajectory　patterns.　However,　most　existing　trajectory　analysis　focuses　only　on　the　spatial　and　temporal　analyses　of　the　raw　trajectory　data　and　misses　essential　semantic　information　concerning　behaviors.　In　this　paper,　we　propose　a　new　trajectory　semantics　calculation　method　to　identify　groups　with　similar　behaviors.　We　first　propose　a　fast　and　efficient　two-phase　method　for　identifying　stay　regions　within　daily　trajectories　and　enriching　the　stay　regions　with　semantic　labels　based　on　points　of　interest　to　generate　semantic　trajectories.　Furthermore,　we　design　a　semantic　similarity　measure　model　using　geographic　and　semantic　similarity　factors　to　measure　the　similarity　between　semantic　trajectories.　We　also　propose　a　pruning　strategy　using　time　entropy　to　decrease　the　number　of　complex　calculations　and　comparisons　to　improve　performance.　The　results　of　our　extensive　experiments　on　the　real　trajectory　dataset　of　the　Geolife　project　show　that　our　proposed　method　is　both　effective　and　efficient.