In　the　heterogeneous　cellular　networks　(HCN)　environment,　users　travel　between　different　cells　to　ensure　that　the　network　connection　will　not　be　interrupted,　and　there　is　a　need　for　network　handover　management　for　users.　In　HCN,　adopting　the　same　handover　strategy　for　different　cells　will　reduce　handover　performance.　Therefore,　a　reasonable　handover　management　strategy　needs　to　consider　the　mobile　preference　and　mobility　characteristics　of　users　in　hot　spots.　Aiming　at　the　existing　problems,　in　this　study,　the　self-similar　least-action　human　walk　(SLAW)　is　analyzed　and　a　method　based　on　the　hidden　Markov　model　(HMM)　for　perceiving　user　behavior　in　hot　spots　is　proposed.　First,　users＇　mobile　paths　in　hot　spots　are　simulated　based　on　SLAW,　and　user　behaviors　are　modeled　using　HMM.　Then,　the　corresponding　moving　time　is　predicted　by　the　mobile　sequence　of　the　user.　Finally,　the　effects　of　different　sampling　times　and　different　base　station　densities　on　the　behavior　prediction　are　analyzed　through　simulation　experiments,　providing　specific　parameter　settings　for　designing　a　reasonable　handover　management　plan.　Meanwhile,　the　prediction　of　user　movement　time　can　ensure　that　the　base　stations　in　hot　spots　will　make　effective　preparations　for　the　upcoming　handover　requests.　©　2018　Totem　Publisher,　Inc.　All　rights　reserved.