Two-sided　matching　decision-making　(TSMDM)　problems　widely　exist　in　human　being＇s　life.　In　practical　TSMDM　problems,　subjects　who　are　being　matched　with　usually　tend　to　provide　linguistic　evaluations　for　convenience.　Owing　to　the　fuzziness　of　decision-making　environment,　subjects　may　provide　several　linguistic　terms　associated　with　their　probabilities　as　the　evaluations,　which　can　be　described　as　the　probabilistic　linguistic　term　sets　(PLTSs).　To　model　such　scenarios,　this　paper　proposes　a　TSMDM　approach　with　probabilistic　linguistic　evaluations.　The　probabilistic　linguistic　evaluations　are　firstly　normalized　with　the　preservation　of　ignored　probabilities.　The　normalized　evaluations　are　then　aggregated　into　the　comprehensive　evaluations　by　using　the　defined　Choquet　integral-based　probabilistic　linguistic　aggregation　(CIPLA)　operator.　Thereafter,　the　satisfaction　degrees　of　two　sides　of　subjects　are　calculated　based　on　the　closeness　coefficient　in　TOPSIS　method.　On　the　basis　of　this,　a　multi-objective　TSMDM　model　aiming　to　maximize　the　comprehensive　satisfaction　degree　is　built　and　transformed　into　a　single-objective　TSMDM　model　considering　the　weights　of　two　sides.　To　solve　the　single-objective　TSMDM　model　is　to　determine　the　optimal　matching　result.　An　illustrative　example　of　matching　enterprises　and　knowledge-sender　universities　is　introduced　to　validate　the　proposed　approach　and　compare　it　with　other　existing　TSMDM　approaches.　The　results　illustrate　that　the　proposed　approach　can　not　only　avoid　information　loss,　but　also　effectively　integrate　PLTSs　with　correlative　criteria.