With　the　development　of　GPS　and　the　popularity　of　mobile　devices　with　positioning　capability,　collecting　massive　amounts　of　trajectory　data　is　feasible　and　easy.　The　daily　trajectories　of　moving　objects　convey　a　concise　overview　of　their　behaviors.　Different　social　roles　have　different　trajectory　patterns.　Therefore,　we　can　identify　users　or　groups　based　on　similar　trajectory　patterns　by　mining　implicit　life　patterns.　However,　most　existing　daily　trajectories　mining　studies　mainly　focus　on　the　spatial　and　temporal　analysis　of　raw　trajectory　data　but　missing　the　essential　semantic　information　or　behaviors.　In　this　paper,　we　propose　a　novel　trajectory　semantics　calculation　method　to　identify　groups　that　have　similar　behaviors.　In　our　model,　we　first　propose　a　fast　and　efficient　approach　for　stay　regions　extraction　from　daily　trajectories,　then　generate　semantic　trajectories　by　enriching　the　stay　regions　with　semantic　labels.　To　measure　the　similarity　between　semantic　trajectories,　we　design　a　semantic　similarity　measure　model　based　on　spatial　and　temporal　similarity　factor.　Furthermore,　a　pruning　strategy　is　proposed　to　lighten　tedious　calculations　and　comparisons.　We　have　conducted　extensive　experiments　on　real　trajectory　dataset　of　Geolife　project,　and　the　experimental　results　show　our　proposed　method　is　both　effective　and　efficient.