Aiming　at　the　fuzzy　type　characteristics　between　multi-frequency　spectrums　of　vibration/acoustic　signal　and　mill　load　parameters,　the　redundancy　and　complementarity　in　the　multi-frequency　spectrums,　and　the　difficulty　of　using　latent　structural　selected　model　in　existing　literatures　to　simulate　the　operation　expert　＇listen＇　inference　to　identify　the　mill　load　parameters,　a　soft　sensing　model　of　mill　load　parameters　based　on　adaptive　extraction　and　selection　of　multi-scale　vibration/acoustic　spectrum　characteristics　is　proposed.　The　method　firstly　acquires　multi-scale　spectrum,　then　extracts　and　selects　the　features　of　multi-scale　vibration　and　acoustic　spectrum　adaptively　using　kernel　partial　least　squares　(KPLS)　and　mutual　information　(MI).　Finally,　the　soft　sensing　model　of　mill　load　parameters　is　constructed　by　using　online　clustering,　Madani　fuzzy　model,　banch　and　bound　(BB),　adaptation　weighted　fusion　(AWF)　and　modeling　of　selective　sets.　The　experiments　are　carried　out　in　the　experimental　wet　mill.　The　experimental　results　show　that　the　proposed　model　can　simulate　the　expert＇s　reasoning　mechanism　and　has　better　modeling　accuracy.　©　2019,　Editorial　Office　of　Control　and　Decision.　All　right　reserved.