The　timely　and　effective　detection　of　f-CaO　content　in　cement　clinker　plays　an　important　role　in　the　control　and　optimization　of　cement　clinker　performance.　Presently,　the　traditional　method　of　manual　sampling　and　testing　in　the　laboratory　is　mostly　used　in　cement　plants　to　detect　the　clinker　f-CaO　content.　However,　the　measurement　results　have　a　large　lag.　To　address　the　problem　of　lagging　results　of　traditional　f-CaO　content　detection,　this　paper　developed　a　model　for　f-CaO　content　prediction　using　the　cascade　forest　algorithm　based　on　the　site　data　of　cement　production.　First,　the　fourteen　model　parameters,　for　example,　raw　material　feeding　rate,　decomposing　furnace　outlet　temperature,　rotary　kiln　current,　etc.　were　selected　according　to　the　production　process　of　cement　clinker　and　the　formation　causes　of　clinker　f-CaO　production,　and　then　the　time　series　were　constructed　as　model　inputs　using　sliding　time　windows　to　include　more　improved　time-series　information.　Finally,　the　proposed　prediction　method　was　compared　with　three　classical　machine　learning　models,　namely　support　vector　machine　regression(SVR),　k-nearest　neighbor　(KNN),and　random　forest　(RF).　Results　show　that　the　built　method　can　provide　better　prediction　accuracy　and　generalization　capability　in　predicting　the　f-CaO　content　of　cement　clinker.　©　2025　Beijing　University　of　Technology.　All　rights　reserved.