Parking　problems　caused　by　a　lack　of　parking　spaces　have　exacerbated　traffic　congestion　and　worsened　environmental　pollution.　An　analysis　of　the　cruising　process　for　parking　can　provide　new　perspectives　to　reduce　cruising.　Based　on　a　parking　survey　conducted　in　Beijing,　the　authors　collected　a　large　amount　of　trajectory　data　of　cruising　vehicles.　Then,　fluctuation　indexes　of　trajectories　were　proposed　to　analyse　travellers＇　cruising　processes　for　parking.　The　spectral　clustering　method　based　on　a　hidden　Markov　model　(HMM)　was　used　to　recognise　the　cruising　trajectories.　The　recognition　performance　for　three-dimensional　trajectory　data　is　better.　Cruising　trajectories　for　Clusters　1,　2,　3,　4,　and　6　have　large　fluctuations　and　a　weightier　effect　on　road　traffic.　These　groups　can　be　taken　as　target　groups　for　intelligent　parking　guidance　and　recommendations.　The　recognition　accuracies　for　parking　location　and　parking　status　increase　with　increasing　intercepted　trajectory　lengths.　150　m　from　far　to　near　the　desired　destination　can　be　used　as　a　threshold　of　the　cruising　trajectory　length　to　accurately　predict　travellers＇　parking　location　and　status.　These　research　results　can　be　applied　in　intelligent　parking　systems　to　dynamically　predict　parking　situations,　formulate　parking　guidance　schemes　and　information　release　strategies,　and　improve　parking　efficiency.