The　efficient　and　accurate　model　construction　of　an　organic　Rankine　cycle　(ORC)　system　is　the　key　to　its　analysis,　prediction,　and　optimization.　As　a　typical　multidisturbance　nonlinear　dynamic　system,　the　ORC　system　always　operates　in　a　nonstationary　state.　Under　uncertain　disturbance,　accurately　identifying　the　thermal　power　conversion　process　state　and　quickly　realizing　the　accurate　association　of　various　mappings　are　the　key　considerations　of　constructing　the　data-driven　model　of　the　ORC　system.　From　the　perspective　of　data　selection,　parameter　association,　and　structural　design,　this　study　proposes　a　methodology　for　the　efficient　multilayer　adaptive　self-organizing　modeling　of　ORC　systems.　This　methodology　can　realize　efficient　autonomous　modeling　in　the　whole　design　process　of　the　data-driven　model　of　the　ORC　system.　Moreover,　the　proposed　methodology　can　minimize　the　structural　risk　of　the　model　by　balancing　the　empirical　risk　and　structural　complexity.　By　taking　real　data　as　the　test　base,　the　generalization　ability　and　time　cost　of　the　data　self-selection　layer,　information　self-correlation　layer　and　adaptive　self-organizing　part　of　the　structure　layer　are　evaluated.　Compared　with　the　direct　construction　of　the　ORC　system　data　model,　the　proposed　methodology　can　reduce　the　model　construction　time　cost　by　75.54%　and　improve　the　generalization　ability　by　61.88%.　In　addition,　maximizing　the　generalization　capability　with　minimum　structural　risk　is　an　important　part　of　data-driven　model　construction　of　ORC　systems.　In　this　study,　a　data-driven　model　structural　reliability　assessment　approach　for　ORC　systems　is　proposed.　Then,　the　proposed　adaptive　self-organizing　optimization　methodology　is　verified　on　the　basis　of　the　structural　reliability　assessment　model.　The　multilayer　adaptive　self-organizing　modeling　methodology　proposed　in　this　study　can　provide　new　ideas　and　necessary　theoretical　guidance.