When　the　working　condition　of　a　wet　ball　mill　is　changed,　the　distribution　of　real-time　data　and　modeling　data　is　inconsistent.　It　is　difficult　to　accurately　measure　the　load　parameters　by　using　the　traditional　soft　sensor　algorithm　based　on　historical　data.　Therefore,　a　transfer　learning　strategy　is　introduced,　and　the　robustness　of　the　model　is　improved　by　the　multi　domain　mechanism.　The　process　is　to　preprocess　and　extract　the　characteristics　of　multi　working　conditions　data,　and　the　distribution　of　the　edge　and　the　conditional　distribution　is　obtained　by　joint　distribution　fitting.　Then　the　maximum　mean　discrepancy　is　used　to　measure　the　distribution　of　adaptive　data,　and　the　calculated　results　are　applied　to　the　regression　weighted.　Finally,　the　target　domain　data　is　used　for　load　forecasting.　The　practicability　and　effectiveness　of　the　model　are　illustrated　by　comparing　experiments　and　cross　experiments.　©　2018,　Editorial　Office　of　Control　and　Decision.　All　right　reserved.