Domain　generalization　aims　at　learning　a　model　with　transferable　knowledge　from　one　or　more　source　domain(s)　in　the　presence　of　domain　shift,　enabling　the　model　to　achieve　effective　generalization　for　an　unseen　target　domain.　Most　existing　methods　pursue　domain-invariant　representations　of　samples　to　address　the　challenges　of　heterogeneous　distributions　across　domains.　However,　most　of　such　methods　are　limited　to　simple　data　manipulation　at　the　instance　level　or　computing　style　statistics　in　feature　space　for　distribution　alignment.　Such　operations　fail　to　effectively　capture　the　contextual　semantics　across　domains　from　both　the　intra　and　inter-views.　In　this　paper,　we　propose　contextual　Distribution　Alignment　via　a　Contrastive　Learning　strategy　with　domain　correlation,　called　DACL,　which　sufficiently　exploits　both　intra-　and　inter-domain　invariant　representations　for　image　domain　generalization　classification.　Specifically,　a　new　Fourier-based　augmentation　method　is　developed　to　capture　high-level　semantic　invariant　features.　Second,　a　domain-based　feature　fusion　module　is　further　proposed　to　increase　the　diversity　of　features,　which　mainly　extracts　both　intra-　and　inter-domain　prototypes　via　clustering　to　learn　cross-domain　representations.　Finally,　we　propose　a　contrastive　learning　strategy　that　takes　domain　correlation　into　account,　which　uses　spatial　second-order　statistics　as　a　metric　to　measure　the　relevance　between　multiple　source　domains.　Extensive　experiments　are　conducted　on　two　domain　generalization　tasks　over　six　benchmarks,　demonstrating　that　DACL　achieves　state-of-the-art　performance　against　baseline　models.　A　series　of　ablation　studies　are　performed　and　in-depth　analyses　are　conducted　in　visualization　to　further　verify　the　rationality　and　effectiveness　of　the　proposed　method.