Modeling　time　series　data　is　an　important　issue　in　many　areas.　Reservoir　computing　(RC)　is　a　promising　tool　to　build　time　series　model　due　to　its　dynamic　characteristic　and　simple　training　way.　Asynchronous　deep　reservoir　computing　(ADRC)　is　an　improvement　version　of　the　traditional　RC.　It　solves　time-dependent　tasks　more　efficiently　than　traditional　RC　because　of　its　rich　dynamics　and　flexible　short-term　memory　(STM).　Nevertheless,　it　has　been　an　open　issue　to　design　RC＇s　or　ADRC＇s　reservoir　topology　owing　to　large　amounts　of　random　factors.　To　promote　the　solution　of　this　problem,　the　paper　proposes　a　feature-recombinant　ADRC　(FR-ADRC)　for　modeling　time　series　data.　In　the　FR-ADRC　scheme,　the　first　sub-reservoir　is　designed　as　the　feature-adaptive　layer,　and　a　trainable　matrix　C　is　introduced　into　this　layer.　By　learning　C,　the　singular　value　(SV)　distribution　of　the　first　layer　could　be　adjusted.　Further,　the　principal　components　of　the　reservoir　topology　can　be　extracted　by　the　principal　component　analysis　(PCA).　Then　a　new　temporary　reservoir　is　constructed　by　recombining　these　extracted　components.　The　subsequent　information　processing　is　carried　out　based　on　the　recombinant　reservoir,　which　can　be　adaptive　to　the　input　signals.　The　validity　of　the　FR-ADRC　is　tested　by　modeling　some　numerical　and　real-life　time　series　data.　Experimental　results　show　that　the　proposed　approach　is　better　than　the　traditional　ADRC　in　modeling　precision,　generalization　ability　and　stability.