Due　to　the　heterogeneity　gap　in　multi-view　data,　researchers　have　been　attempting　to　apply　these　data　to　learn　a　co-latent　representation　to　bridge　this　gap.　However,　multi-view　representation　learning　still　confronts　two　challenges:　(1)　it　is　hard　to　simultaneously　consider　the　performance　of　downstream　tasks　and　the　interpretability　and　transparency　of　the　network;　(2)　it　fails　to　learn　representations　that　accurately　describe　the　class　boundaries　of　downstream　tasks.　To　overcome　these　limitations,　we　propose　an　interpretable　representation　learning　framework,　named　interpretable　multi-view　proximity　representation　learning　network.　On　the　one　hand,　the　proposed　network　is　customized　by　an　explicitly　designed　optimization　objective　that　enables　it　to　learn　semantic　co-latent　representations　while　maintaining　the　interpretability　and　transparency　of　the　network　from　the　design　level.　On　the　other　hand,　the　designed　multi-view　proximity　representation　learning　objective　function　encourages　its　learned　co-latent　representations　to　form　intuitive　class　boundaries　by　increasing　the　inter-class　distance　and　decreasing　the　intra-class　distance.　Driven　by　a　flexible　downstream　task　loss,　the　learned　co-latent　representation　can　adapt　to　various　multi-view　scenarios　and　has　been　shown　to　be　effective　in　experiments.　As　a　result,　this　work　provides　a　feasible　solution　to　a　generalized　multi-view　representation　learning　framework　and　is　expected　to　accelerate　the　research　and　exploration　in　this　field.　©　The　Author(s),　under　exclusive　licence　to　Springer-Verlag　London　Ltd.,　part　of　Springer　Nature　2024.