In　recent　years,　mergers　and　acquisitions　(M&A)　have　been　frequent,　but　the　failure　rate　of　M&A　has　remained　high　in　reality.　In　order　to　reduce　the　M&A　failure　rate,　a　key　step　is　to　achieve　an　effective　M&A　matching　which　can　help　integrate　enterprise　resources　and　can　make　companies　bigger　and　stronger.　Given　the　risk　of　poor　M&A　matching　decisions,　it　is　important　to　consider　both　the　rational　and　irrational　behavior　of　decision-makers.　This　study　developed　a　M&A　matching　method　that　considers　irrational　behavior　from　a　DEA-based　performance　perspective.　In　this　method,　the　M&A　party　evaluates　the　other　party　with　itself　as　the　reference　point.　However,　the　approach　also　draws　on　the　idea　of　cross-efficiency　modeling.　In　this　approach,　we　construct　a　data　envelopment　analysis　(DEA)　model　that　has　a　prospect　value　as　the　secondary　optimization　goal.　This　generates　index　weights　to　further　determine　decision　weights.　Then,　the　value　and　decision　weights　are　aggregated　into　a　prospect　value,　which　is　used　to　assess　the　degree　of　satisfactory　matching　to　optimize　the　two-sided　M&A　matching.　We　then　provide　an　example　to　illustrate　the　feasibility　and　effectiveness　of　two-side　M&A　matching.　This　method　can　be　applied　to　third-party　platforms　to　provide　matching　recommendation　services　for　participating　M&A　companies.