Calcium　aluminate　cement　(CAC)　is　an　important　hydraulic　cementitious　material.　It　is　widely　used　in　construction,　metallurgy,　chemical　industry　and　other　fields　due　to　its　high　early　strength.　The　factors　affecting　its　strength　are　also　very　complex.　The　research　focus　of　this　paper　is　to　establish　a　prediction　model　for　the　compressive　strength　of　CAC　paste,　so　as　to　assist　scientific　research　and　practical　engineering　to　quickly　predict　the　strength　of　CAC　paste　at　different　ages　under　different　mix　ratios　and　curing　conditions.　In　this　paper,　273　sets　of　data　are　trained　and　tested　based　on　support　vector　regression　(SVR),　random　forest　regression　(RFR),　gradient　boosting　(GB)　and　extreme　gradient　boosting　(XGB)　algorithms.　It　is　found　that　the　prediction　accuracy　of　GB　model　can　reach　89%.　Meanwhile,　based　on　the　GB　model,　the　feature　importance　analysis,　global　interpretation　and　dependence　analysis　are　carried　out.　It　is　found　that　the　main　factors　affecting　the　strength　of　CAC　are　relative　humidity,　silica　fume　content　and　curing　temperature.　To　obtain　high-strength　CAC　paste,　the　recommended　mix　ratio　and　curing　conditions　are　as　follows:　Al2O3　content　is　67%,　CaO　content　is　32%,　silica　fume　replacement　rate　is　10%,　water-cement　ratio　is　0.1,　relative　humidity　is　90%,　curing　temperature　is　5　degrees　C　and　low-temperature　treatment　time　is　greater　than　60　days.　Finally,　a　graphical　user　interface　is　established　to　facilitate　direct　prediction　of　CAC　paste　under　new　mix　ratio　and　curing　conditions.