Sampled-data　systems　(SDSs)　have　received　extensive　attention　due　to　their　wide　application　in　industrial　processes.　However,　for　SDSs　characterized　by　complex　nonlinear　dynamics,　it　is　still　a　great　challenge　to　achieve　stable　tracking　control　when　they　are　affected　by　stochastic　sampling.　To　deal　with　this　situation,　a　data-based　adaptive　model　predictive　control　(DAMPC)　method　is　developed　to　stabilize　the　stochastic　sampled-data　complex　nonlinear　systems　(SSDCNSs).　First,　an　equivalent　system　with　stochastic　time-varying　delay　is　constructed　to　describe　SSDCNS.　Then,　the　sampling　interval　variation　of　SSDCNS　is　equivalently　converted　into　the　stochastic　time-varying　delay,　whose　transfer　probability　can　be　gained　by　the　activation　frequencies　of　stochastic　sampling　intervals.　Second,　a　fuzzy　neural　network　(FNN)-based　multistep　predictive　model　with　an　adaptive　prediction　horizon　(APH)　is　established.　Then,　APH　is　adaptively　adjusted　according　to　the　stochastic　time-varying　delay　and　its　transfer　probability,　and　the　necessary　predictive　information　can　be　provided　for　the　controller.　Third,　an　optimal　control　problem　(OCP)　is　solved　to　stabilize　the　SSDCNS.　Especially,　an　attenuation　learning　rate　(ALR)　is　designed　for　the　controller　to　reduce　excessive　control　increments.　Then,　the　control　action　can　be　calculated　to　realize　stable　tracking　control.　Finally,　the　stability　of　the　proposed　scheme　is　analyzed　in　theory,　and　the　effectiveness　of　the　designed　method　is　assessed　by　a　numerical　simulation　system　and　an　industrial　application　in　the　wastewater　treatment　process　(WWTP).