Due　to　the　unreliable　communication　network,　networked　control　systems　(NCSs)　are　often　subjected　to　communication　imperfections　including　stochastic　sampling　intervals　(SSIs)　and　packet　dropouts,　which　can　lead　to　degradation　of　control　performance　and　even　jeopardize　the　stability　of　the　NCSs.　In　this　article,　the　data-driven　model　predictive　control　(DMPC)　strategy　is　proposed　to　stabilize　a　class　of　unknown　nonlinear　NCSs　with　SSIs　and　successive　packet　dropouts　(SPDs).　First,　an　equivalent　stochastic　sampling　model　is　constructed　by　capturing　the　randomness　of　both　SSIs　and　SPDs,　which　can　determine　the　probability　information　of　the　equivalent　sampling　interval　between　consecutive　no-packet　-dropout　update　instants.　Subsequently,　a　multimodel　predictive　structure　is　designed　based　on　the　possible　lengths　of　the　sampling　interval　in　the　equivalent　stochastic　sampling　model,　which　can　provide　predictive　outputs　for　the　subsequent　controller　design.　Furthermore,　to　reduce　the　computational　burden　associated　with　the　prolongation　of　the　prediction　horizon　in　the　multimodel　predictive　structure,　a　prediction　data　interpolation　algorithm　based　on　the　Lagrange　interpolation　polynomial　is　introduced.　Additionally,　to　ensure　the　accuracy　of　interpolation　prediction　output,　an　adaptive　mechanism　for　updating　interpolation　nodes　is　designed　to　dynamically　adjust　the　number　and　position　of　these　nodes.　Finally,　a　cost　function　that　relies　on　the　expectation　of　the　predictive　output　is　designed　and　solved　to　achieve　stable　tracking　control　of　the　considered　NCSs.　The　stability　of　the　DMPC　strategy　is　demonstrated　in　detail.　Numerical　examples　and　industrial　applications　for　the　wastewater　treatment　process　(WWTP)　demonstrate　that　DMPC　can　obtain　satisfied　control　performance.　©　2013　IEEE.