The　learning　curve　is　a　powerful　tool　for　estimating　either　the　production　cost　or　time　in　production　analysis.　Our　work　focuses　on　the　characterization　of　the　uncertain　nature　of　the　learning　effect.　Traditionally,　the　learning　curve　of　a　new　product　can　be　derived　by　processing　the　past　data　obtained　for　similar　products.　However,　due　to　the　complicated　environment,　in　some　cases,　the　data　for　similar　products　may　be　lacking,　preventing　the　use　of　the　traditional　methods　to　find　the　learning　curve.　To　address　this　issue,　we　develop　an　uncertain　learning　curve　by　utilizing　uncertainty　theory.　Moreover,　some　useful　theorems　are　developed　to　characterize　the　uncertain　learning　curve.　An　experiment　is　conducted　to　compare　the　proposed　model　and　the　standard　model.　Finally,　we　apply　the　uncertain　learning　curve　to　the　single-machine　optimization　problem.