To　address　the　problem　that　it　is　difficult　to　obtain　effective　deep　learning　classification　models　in　the　absence　of　a　large　amount　of　consistent　distribution　and　labeled　data　in　the　target　application　scenario,　a　domain　adaptation　framework　was　proposed　based　on　explicit　feature　alignment　with　domain　adversarial　and　implicit　category　alignment.　The　advantages　of　the　domain　distribution　difference　approach　and　the　adversarial　learning　approach　were　combined　in　this　framework.　For　the　explicit　feature　alignment　module,　considering　the　optimization　challenges　caused　by　large　differences　in　domain　knowledge,　an　incremental　collaborative　optimization　strategy　was　used.　The　goal　of　this　strategy　is　to　improve　the　domain　adaptation　performance　by　reducing　the　domain　differences　between　different　semantic　layers.　For　the　implicit　category　alignment　module,　to　enhance　the　discriminability　of　the　target　features,　a　self-training　method　was　adopted　to　obtain　pseudo-labels　and　overcome　the　problem　of　label　noises.　Besides,　the　accuracy　of　the　pseudo-labels　was　optimized　by　learning　the　confusion　matrix,　thus　a　target　domain　loss　function　was　automatically　constructed.　This　function　is　able　to　reduce　the　differences　between　domains,　while　improving　the　accurate　alignment　of　the　feature　distribution　of　the　same　categories　in　both　the　source　and　target　domains.　Experiments　based　on　6　cross-domain　classification　tasks　on　the　Office-31　dataset　and　12　sets　of　cross-domain　classification　tasks　on　the　Office-Home　dataset　were　conducted.　Results　show　that　the　average　classification　accuracy　of　the　proposed　method　on　the　transfer　learning　tasks　improves　by　11.　9%　and　19.　9%,　respectively,　compared　with　the　benchmark　method,　demonstrating　the　effectiveness　of　the　network　in　domain　adaptation　tasks.　©　2023　Beijing　University　of　Technology.　All　rights　reserved.