Cross-domain　sentiment　analysis　(CDSA)　aims　to　learn　transferable　knowledge　from　the　source　domain　to　facilitate　the　sentiment　polarity　classification　on　the　target　domain　of　lacking　labeled　data.　Currently,　two　types　of　unsupervised　domain　adaptation　(UDA)　methods　are　widely　used　in　CDSA　tasks.　One　employs　the　domain　adversarial　strategy　to　extract　domain-invariant　features,　and　the　other　utilizes　the　distance　metric　strategy　to　reduce　domain　distribution　discrepancy.　However,　the　fine-grained　domain-specific　information　related　to　categories　aligned　between　domains　is　not　preserved,　which　suppresses　the　performance　of　target-domain　classification.　To　overcome　the　mentioned　problem,　a　unified　Domain　Adversarial　Category-wise　Alignment　Network　(DACAN)　was　proposed　in　this　paper.　An　integrated　network　was　constructed　with　progressive　multi-level　feature　learning.　Specifically,　a　feature　extraction　module　was　constructed　with　parameter　sharing　between　two　domains　at　low-level　text　feature　extraction　layers.　The　domain　adversarial　module　was　added　to　enable　shared　knowledge　transfer　by　extracting　domain-invariant　information　and　by　updating　the　shared　parameters　at　the　feature　extraction　layers.　A　category-wise　alignment　module　was　built　to　achieve　local　distribution　alignment　at　the　high　dimension-level　semantic　layers　guided　by　fine-grained　category　structure　information.　Meanwhile,　joint　constraint　was　established　with　domain-invariant　constraint　based　on　domain　adversarial,　and　domain-consistency　constraint　based　on　category-wise　alignment.　Comprehensive　experiments　were　conducted　on　two　standard　Amazon　review　datasets.　The　results　show　that　DACAN　outperforms　other　state-of-the-art　UDA　methods　by　0.7%　and　1.1%　on　the　two-and　three-category　CDSA　tasks,　respectively.　Also,　better　performance　results　are　achieved　with　a　synergistic　UDA　scheme　compared　with　a　single　UDA　scheme.