Based　on　Support　Vector　Regression,　Random　Forest　Regression,　Gradient　Boosting　and　Extreme　Gradient　Boosting,　a　prediction　model　of　concrete＇s　freeze-thaw　mass　loss　rate　with　high　accuracy　and　strong　generalization　ability　is　established.　The　impacts　of　water,　cement,　fly　ash,　slag,　sand,　natural　coarse　aggregate,　recycled　coarse　aggregate,　superplasticizer,　air-entraining　agent,　and　the　number　freeze-thaw　cycles　on　the　freeze-thaw　resistance　of　concrete　are　systematically　analyzed　using　feature　importance　analysis,　global　interpretation　and　partial　dependence　analysis.　The　main　results　are　as　follows:　(1)　The　Gradient　Boosting　and　Extreme　Gradient　Boosting　models　demonstrate　relatively　higher　prediction　accuracy,　with　the　Extreme　Gradient　Boosting　model　exhibiting　stronger　generalization　ability　compared　to　the　Gradient　Boosting　model.　(2)　Sand,　natural　coarse　aggregate　and　the　number　freeze-thaw　cycles　have　the　most　substantial　impact　on　the　freeze-thaw　resistance　of　concrete,　while　the　impact　of　recycled　coarse　aggregate　is　relatively　modest.　Utilizing　suitable　quantities　of　air-entraining　agent,　fly　ash,　slag,　and　superplasticizer　can　improve　concrete＇s　freeze-thaw　resistance.　(3)　A　reduced　water-cement　ratio　is　advantageous　for　enhancing　concrete＇s　resistance　to　freeze-thaw　damage　in　cold　regions.　(4)　The　partial　dependence　analysis　of　Extreme　Gradient　Boosting　model　can　provide　references　for　optimizing　the　composition　of　each　component　during　the　preparation　of　concrete　in　cold　regions.　(5)　A　graphical　user　interface　based　on　the　Extreme　Gradient　Boosting　model　for　predicting　concrete　freeze-thaw　mass　loss　rate　has　been　developed,　facilitating　user-friendly　interaction　with　the　computer　program　and　enhancing　the　practicality　of　the　model.　©　2024　Elsevier　Ltd