This　paper　presents　a　supervised　learning　method　for　the　physical　field　reconstruction　in　a　specific　heat　transfer　problem.　The　deep　convolutional　neural　network　(CNN)　is　applied　to　predict　fields　from　a　few　measurable　information,　while　heat　transfer　characteristics　of　interest　can　be　then　easily　inferred　from　the　fields.　This　data-driven　method　can　establish　an　end　to　end　mapping　from　low-dimensional　measurable　information　to　full　physical　fields.　Two　modes　of　measurable　information　are　considered　as　inputs　of　the　network.　When　the　measurable　information　is　an　accurate　structure　or　work　condition　parameters,　this　method　is　equivalent　as　an　efficient　surrogate　model　instead　of　computational　fluid　dynamics　(CFD)　simulation.　This　network　can　also　reconstruct　the　full-field　from　local　information　with　several　measuring　points　as　inputs.　To　our　best　knowledge,　this　is　the　first　time　a　CNN　based　model　has　been　used　as　a　high-fidelity　field　predicator　for　the　flow　heat　transfer.　To　validate　this　method,　the　fields　of　Al2O3-water　nanofluid　laminar　flow　in　a　grooved　microchannel　are　employed　to　be　reconstructed　from　a　set　of　reduced　parameters.　It　indicates　that　the　reconstruction　model　enables　accurate　results　for　all　the　temperature,　velocity　and　pressure　fields.　Meanwhile,　the　characteristics　concerned　in　a　heat　transfer　process,　such　as　Nu　and　f,　can　also　be　extracted　from　the　reconstructed　fields　with　high　precision.　Furthermore,　the　reconstruction　performance　and　stability　are　verified　from　several　perspectives,　including　the　loss　function,　train-data　size,　measuring　noise　and　points　layout.　At　last,　the　comparison　of　computational　costs　shows　that　a　well-trained　CNN　model　has　three　orders　of　magnitude　faster　than　CFD　solver.　The　proposed　approach　can　provide　an　efficient　analysis　tool　with　acceptable　accuracy　for　heat　transfer　research.　©　2020　Elsevier　Ltd