In　the　context　of　heterogeneous　cellular　networks　(HCN),　to　solve　the　difficult　problems　that　traditional　handover　management　strategy　rarely　comprehensively　considers　the　mobile　preferences　and　features　of　users　in　the　hot-spot　areas,　based　on　hidden　Markov　model　(HMM),　an　approach　of　sensing　hot-spot　area　user　behaviors　was　proposed　in　this　paper.　First,　based　on　self-similar　least-action　human　walk　(SLAW),　the　movement　paths　of　hot-spot　area　users　were　simulated　and　a　modeling　for　users　was　created　by　using　HMM.　Then,　the　movement　time　was　predicted　by　referring　to　users＇　movement　sequence.　Finally,　simulation　experiment　was　conducted　to　analyze　the　impact　of　different　sampling　time　and　base　station　densities　on　behavior　predictions.　As　a　result,　specific　setting　parameters　were　provided　for　designing　reasonable　handover　management　plans.　It　turns　out　that　this　approach　improves　the　accuracy　of　the　prediction　of　user　movement　time　to　make　sure　that　hot-spot　area　base　stations　can　be　properly　prepared　for　the　upcoming　user　switch　request.　©　2019,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.