The　cross-efficiency　method　is　an　effective　way　to　rank　decision-making　units　(DMUs)　in　data　envelopment　analysis.　The　traditional　approach　for　cross-efficiency　aggregation　relies　on　an　equally　weighted　average　that　ignores　their　relative　importance.　Although　many　aggregation　methods　based　on　prospect　theory　and　Shannon　entropy　have　been　proposed　by　scholars,　there　are　still　some　drawbacks　in　existing　cross-efficiency　aggregation　approaches.　First,　the　subjective　weighting　method　based　on　prospect　theory　cannot　reflect　the　preference　of　the　decision　maker　(DM)　in　a　more　flexible　way.　Second,　the　determination　of　aggregation　weights　only　considers　a　single　perspective　that　may　not　comprehensively　reflect　the　decision　information.　To　address　these　deficiencies,　this　study　proposes　a　new　method　for　deriving　meaningful　aggregation　weights　from　subjective　and　objective　perspectives.　From　a　subjective　perspective,　prospect　theory　is　introduced　to　reflect　the　preference　of　DM,　and　this　method　provides　an　interval　of　reference　point　that　is　able　to　select　such　a　reference　point　in　light　of　the　DMs＇　preferences　and　decision　goals.　The　idea　of　variance　is　then　used　to　reflect　the　degree　of　deviation　between　peer-evaluation　efficiency　and　self-evaluation　efficiency,　and　objective　weights　are　obtained.　Moreover,　an　optimization　model　is　constructed　to　obtain　integrated　weights　that　reflect　both　the　subjective　preference　of　the　DM　and　the　intrinsic　objective　information　contained　in　the　cross-efficiency　matrix.　Finally,　two　numerical　examples　are　examined　to　illustrate　the　effectiveness　and　rationality　of　the　proposed　method.