Due　to　the　inherent　characteristics　of　sensor　nodes　in　Internet　of　Things,　such　as　constrained　energy,　data　redundancy,　limited　communication　range　and　computing　capabilities,　the　data　loss　problem　becomes　one　key　issue　for　applications　which　depends　heavily　on　the　data　completeness.　Some　of　the　current　solutions,　such　as　interpolation,　are　designed　to　use　data　correlation　for　the　data　recovery　problem.　Spatiotemporal　correlation　is　an　important　characteristic　of　sensory　data　since　the　nodes　are　generally　deployed　to　observed　similar　physical　phenomenon.　However,　it　is　very　difficult　to　extract　data　correlation　especially　in　case　that　the　coupling　degree　between　different　perceptual　attributes　is　low.　Machine　learning　is　an　efficient　auto-learning　method　that　can　obtain　the　inherent　rules　automatically.　This　paper　has　proposed　an　intelligent　recovery　scheme　for　big　data　in　Internet　of　Things　based　on　Multi-Attribute　assistance　and　Extremely　randomized　Trees　(MAET).　Firstly,　the　collected　dataset　is　denoised　by　detecting　and　removing　the　outliers.　Secondly,　the　slave　attributes　are　chosen　whose　correlations　are　high　with　the　target　attribute　by　using　the　Spearman　correlation　coefficient.　Thirdly,　the　proposed　scheme　is　trained　by　using　extremely　randomized　trees　with　slave　attributes.　Finally,　the　missing　data　can　be　recovered　with　the　trained　model　as　well　as　the　help　of　other　attributes　whose　data　is　not　lost.　Experiment　shows　that　the　proposed　scheme　with　multiple　attributes　is　efficient　and　can　improve　the　accuracy　of　recovered　data　compared　with　other　algorithms.　©　2020　Elsevier　Inc.