With　the　widespread　use　of　the　location-based　social　networks　(LBSNs),　the　next　point-of-interest　(POI)　recommendation　has　become　an　essential　service,　which　aims　to　understand　the　user＇s　check-in　behavior　at　the　current　moment　by　analyzing　and　mining　the　correlations　between　the　user＇s　check-in　behaviors　within　his/her　historical　trajectory　and　then　recommending　the　POI　that　the　user　is　most　likely　to　visit　at　the　next　time　step.　However,　the　user＇s　check-in　trajectory　presents　extremely　irregular　sequential　patterns,　such　as　spatial-temporal　patterns,　semantic　patterns,　etc.　Intuitively,　the　user＇s　visiting　behavior　is　often　accompanied　by　a　certain　purpose,　which　makes　the　check-in　data　in　LBSNs　often　have　rich　semantic　activity　characteristics.　However,　existing　research　mainly　focuses　on　exploring　the　spatial-temporal　sequential　patterns　and　lacks　the　mining　of　semantic　information　within　the　trajectory,　so　it　is　difficult　to　capture　the　user＇s　visiting　intention.　In　this　paper,　we　propose　a　self-attention-　and　multi-task-based　method,　called　MSAN,　to　explore　spatial-temporal　and　semantic　sequential　patterns　simultaneously.　Specifically,　the　MSAN　proposes　to　mine　the　user＇s　visiting　intention　from　his/her　semantic　sequence　and　uses　the　user＇s　visiting　intention　prediction　task　as　the　auxiliary　task　of　the　next　POI　recommendation　task.　The　user＇s　visiting　intention　prediction　uses　hierarchical　POI　category　attributes　to　describe　the　user＇s　visiting　intention　and　designs　a　hierarchical　semantic　encoder　(HSE)　to　encode　the　hierarchical　intention　features.　Moreover,　a　self-attention-based　hierarchical　intention-aware　module　(HIAM)　is　proposed　to　mine　temporal　and　hierarchical　intention　features.　The　next　POI　recommendation　uses　the　self-attention-based　spatial-temporal-aware　module　(STAM)　to　mine　the　spatial-temporal　sequential　patterns　within　the　user＇s　check-in　trajectory　and　fuses　this　with　the　hierarchical　intention　patterns　to　generate　the　next　POI　list.　Experiments　based　on　two　real　datasets　verified　the　effectiveness　of　the　model.