The　construction　of　organic　Rankine　cycle　(ORC)　system　model　is　the　key　to　system　performance　analysis　and　prediction.　However,　traditional　analysis　methods　have　obvious　limitations　in　constructing　strong　coupling　relationship　between　operating　parameters　and　performance　due　to　the　complex　thermal　power　conversion　process　of　ORC　system.　First,　this　study　systematically　analyzes　the　nonlinear　relationship　between　ORC　system　operating　parameters　and　performance　by　using　unsupervised　learning　and　bilinear　interpolation　algorithm.　Compared　with　the　traditional　thermodynamic　modeling　method,　the　artificial　neural　network　(ANN)　has　obvious　advantages　in　constructing　the　mapping　relationship　of　ORC　system.　However,　the　ORC　system　prediction　model　still　has　the　defects　of　low　accuracy,　poor　robustness,　and　high　time　cost　due　to　the　absence　of　outlier　removal　and　feature　dimensionality　reduction.　A　hybrid　algorithm　for　ORC　system　prediction　model　construction　is　proposed　on　the　basis　of　the　data　characteristics,　information　theory　and　unsupervised　learning.　This　algorithm　can　remove　outliers　and　reduce　the　dimensionality　of　features　in　ORC　system　simultaneously.　Then,　the　effectiveness　of　outlier　removal,　feature　dimensionality　reduction,　and　overall　performance　of　the　hybrid　algorithm　is　verified.　The　mean　squared　error　and　mean　absolute　percentage　error　of　the　model　is　1.64　x　10(-11)　and　5.1　x　10(-3)%.　Compared　with　other　algorithms,　the　hybrid　algorithm　suitable　for　ORC　system　has　improved　in　accuracy　and　time　cost.　The　accuracy　of　the　hybrid　algorithm　is　improved　by　5.56%　at　least.　The　time　cost　of　the　hybrid　algorithm　is　reduced　by　at　least　17.05%.　The　hybrid　algorithm　can　provide　direct　guidance　for　constructing　ANN　model　of　ORC　system.