This　paper　aims　to　propose　a　two-sided　matching　decision-making　approach　with　the　probabilistic　linguistic　evaluations.　Existing　two-sided　matching　decision-making　methods　rarely　consider　the　psychological　behaviors　of　the　subjects,　which　makes　the　matching　result　deviate　from　the　reality.　To　overcome　this　drawback,　we　introduce　the　prospect　theory　to　describe　the　perception　of　the　subjects.　At　first,　the　probabilistic　linguistic　evaluations　are　normalized.　Then,　the　evaluations　under　the　cost　criteria　are　transformed　into　their　benefit　types　to　guarantee　the　consistency　of　the　computation　process.　Thereafter,　calculate　the　prospect　values　for　the　subjects　based　on　the　defined　relative　normalized　Lance　distance　of　the　probabilistic　linguistic　term　sets.　Afterwards,　aggregate　the　prospect　values　into　the　satisfaction　degrees.　On　the　basis　of　this,　build　the　multi-objective　two-sided　matching　decision-making　model　and　further　transform　it　into　the　single-objective　model.　To　solve　the　latter　is　to　obtain　the　optimal　matching　result.　An　illustrative　example　of　intelligent　technology　transfer　is　presented　to　validate　the　proposed　approach,　and　the　optimal　matching　result　is　obtained　for　the　demanders　and　the　providers.　Finally,　we　demonstrate　the　advantages　of　the　proposed　approach　comparing　with　the　existing　methods.