In　recent　years,　the　free-floating　bike-sharing　(FFBS)　system　has　become　a　significant　mode　of　travel　to　satisfy　urban　residents＇　travel　demands.　However,　with　the　rapid　development　of　FFBS,　many　problems　have　arisen,　among　which　the　parking　problem　is　the　most　prominent.　To　solve　the　FFBS　parking　problem　around　urban　subways,　firstly,　the　time　series　of　FFBS　parking　pattern　and　subway　station　classification　in　Beijing　were　constructed　based　on　parking　intensity,　showing　a　significant　spatial　distribution　of　subway　stations　with　different　intensity　levels.　Second,　a　hierarchical　clustering　method　based　on　dynamic　time　warping　(DTW)　was　proposed　to　cluster　the　FFBS　parking　time　series.　Subway　stations　in　Beijing　were　grouped　into　11　clusters,　and　the　clustering　purity　reached　0.939,　which　achieved　the　expected　effect.　Then,　the　peak　and　off-peak　period　features　of　time　series　were　extracted　to　discuss　the　clustering　results.　Finally,　a　two-level　early-warning　index　for　monitoring　FFBS　was　constructed,　which　took　the　real-time　parking　quantity　and　land　use　capacity　of　FFBS　into　consideration.　And　FFBS　parking　management　strategies　for　different　early-warning　indices　were　put　forward.　It　is　very　important　for　the　sustainable　development　of　FFBS　and　cities.