In　the　field　of　civil　engineering　education,　accurately　evaluating　the　effectiveness　of　budget　courses　is　crucial.　However,　traditional　methods　of　evaluation　tend　to　be　cumbersome　and　subjective.　In　recent　years,　machine　learning　technology　has　demonstrated　immense　potential　in　educational　evaluation.　Nevertheless,　in　practical　application,　the　machine　learning-based　evaluation　model　for　civil　engineering　budget　courses　faces　the　predicament　of　inadequate　evaluation　accuracy.　To　solve　this　problem,　the　squirrel　search　algorithm　technology　was　used　to　establish　support　vector　machine　parameters　and　create　optimization　algorithms.　The　performance　of　the　proposed　optimization　algorithm　was　tested,　and　the　results　showed　that　the　accuracy　of　the　proposed　algorithm　was　0.927,　which　was　better　than　similar　prediction　algorithms.　Then,　the　empirical　analysis　of　the　proposed　civil　engineering　budget　course　evaluation　model　showed　that　student　satisfaction　and　student　examination　scores　had　increased　to　92　and　94　points,　respectively.　The　above　results　reveal　that　the　proposed　optimization　algorithm　and　course　evaluation　model　have　good　performance.　Therefore,　the　implementation　of　the　proposed　curriculum　evaluation　method　can　significantly　improve　the　learning　efficiency　of　students　and　the　teaching　quality　of　civil　engineering　budgeting　methods　courses.　©　2024　the　author(s),　published　by　De　Gruyter.