For　deep　domain　adaptation　issues,　redundant　information　in　feature　representation　causes　poor　model　performance.　A　bi-classifier　domain　adaptation　model　was　proposed　based　on　contrastive　learning.　Based　on　the　theory　of　bi-classifier　learning,　the　input　data　twice　was　enhanced　to　obtain　the　features　from　two　views,　and　the　diversity　of　classifiers　was　improved　by　inputting　features　of　different　perspectives　into　different　classifiers.　At　the　same　time,　by　closely　combining　the　bi-classifier　method　and　contrast　learning,　the　model　was　able　to　capture　high-level　semantic　representations　of　the　data,　and　reduce　the　confusion　degree　between　feature　from　different　class.　Finally,　the　samples　were　recognized　by　the　proposed　model　at　classification　boundary　correctly　by　aligning　the　label　distribution.　Experimental　results　verify　that　the　contrastive　loss　between　two　classifiers　can　extract　valid　information　from　the　data,　thereby　improving　model　performance.　©　2023　Beijing　University　of　Technology.　All　rights　reserved.