This　paper　performs　the　data-driven　research　on　the　autogenous　shrinkage　of　Ultra-High　Performance　Concrete　(UHPC)　based　on　multiple　machine　learning　algorithms.　The　autogenous　shrinkage　prediction　model　of　UHPC　with　high　prediction　accuracy　(R2　=　0.89)　and　strong　generalization　capability　is　established　based　on　Gradient　Boosting　(GB)　algorithm.　The　Graphic　User　Interface　(GUI)　of　UHPC　autogenous　shrinkage　prediction　is　designed,　which　assists　in　scientific　research　and　engineering　applications.　Cement,　silica　fume,　water,　superabsorbent　polymer,　and　expansive　agent　contents　that　are　closely　related　to　hydration　process　have　prominent　impacts　on　autogenous　shrinkage　of　UHPC,　while　the　fly　ash,　steel　fibers,　and　sand　contents　show　the　slight　impacts.　The　additions　of　superabsorbent　polymer,　expansive　agent,　steel　fibers,　sand,　and　superplasticizer　effectively　reduce　the　autogenous　shrinkage　of　UHPC.　Lower　water　to　cement　ratio　causes　larger　autogenous　shrinkage　of　UHPC.