Accurately　predicting　the　next　location　of　urban　individuals　is　a　central　issue　in　human　mobility　research.　Human　mobility　exhibits　diverse　patterns,　requiring　the　integration　of　spatiotemporal　contexts　for　location　prediction.　In　this　context,　multi-view　learning　has　become　a　prominent　method　in　location　prediction.　Despite　notable　advances,　current　methods　still　face　challenges　in　effectively　capturing　non-spatial　proximity　of　regional　preferences,　complex　temporal　periodicity,　and　the　ambiguity　of　location　semantics.　To　address　these　challenges,　we　propose　a　representation-enhanced　multi-view　learning　network　(ReMVL-Net)　for　location　prediction.　Specifically,　we　propose　a　community-enhanced　spatial　representation　that　transcends　geographic　proximity　to　capture　latent　mobility　patterns.　In　addition,　we　introduce　a　multi-granular　enhanced　temporal　representation　to　model　the　multi-level　periodicity　of　human　mobility　and　design　a　rule-based　semantic　recognition　method　to　enrich　location　semantics.　We　evaluate　the　proposed　model　using　mobile　phone　data　from　Fuzhou.　Experimental　results　show　a　2.94%　improvement　in　prediction　accuracy　over　the　best-performing　baseline.　Further　analysis　reveals　that　community　space　plays　a　key　role　in　narrowing　the　candidate　location　set.　Moreover,　we　observe　that　prediction　difficulty　is　strongly　influenced　by　individual　travel　behaviors,　with　more　regular　activity　patterns　being　easier　to　predict.