Fe-based　amorphous　coatings　prepared　by　high-velocity　oxy-fuel　(HVOF)　spraying　have　the　advantages　of　good　mechanical　properties,　high　density,　low　porosity,　and　high　amorphous　content.　The　service　life　and　bonding　strength　of　coating　greatly　depend　on　its　thickness;　however,　the　characterization　of　ferromagnetic　coating　thickness　is　a　very　difficult　problem.　Pulsed　eddy　current　(PEC)　is　characterized　by　abundant　signals　in　frequency　domains.　In　this　paper,　the　thickness　measurement　principle　of　ferromagnetic　coating　was　explored,　and　the　typical　and　entropy　features　from　PEC　signals　were　extracted.　Seven　integrated　learning　methods　were　combined　to　quantitatively　characterize　the　coating　thickness,　namely　ridge　regression　(RR),　lasso　regression　(LR),　random　forest　regression　(RFR),　extra　trees　regression　(ETR),　gradient　boosting　tree　regression　(GBTR),　addaptive　boost　regression　(ABR)　and　eXtreme　Gradient　Boosting　Regression　(XGBR)　algorithms.　By　comparing　typical　features　with　new　ones,　it　was　verified　that　the　effective　combination　of　entropy　features　and　typical　features　could　be　used　as　effective　feature　parameters　of　eddy　current　signal.　Statistical　scores　(RMSE　and　R-2)　and　GridsearchCV　features　were　used　to　evaluate　and　optimize　the　established　model.　As　indicated　by　the　results,　the　proposed　XGBR　machine　learning　model　well　predicted　the　coating　thickness,　and　the　relative　error　less　than　0.05　mm.