Long　Document　Classification　(LDC)　has　attracted　great　attention　in　Natural　Language　Processing　and　achieved　considerable　progress　owing　to　the　large-scale　pre-trained　language　models.　In　spite　of　this,　as　a　different　problem　from　the　traditional　text　classification,　LDC　is　far　from　being　settled.　Long　documents,　such　as　news　and　articles,　generally　have　more　than　thousands　of　words　with　complex　structures.　Moreover,　compared　with　flat　text,　long　documents　usually　contain　multi-modal　content　of　images,　which　provide　rich　information　but　not　yet　being　utilized　for　classification.　In　this　article,　we　propose　a　novel　cross-modal　method　for　long　document　classification,　in　which　multiple　granularity　feature　shifting　networks　are　proposed　to　integrate　the　multi-scale　text　and　visual　features　of　long　documents　adaptively.　Additionally,　a　multi-modal　collaborative　pooling　block　is　proposed　to　eliminate　redundant　fine-grained　text　features　and　simultaneously　reduce　the　computational　complexity.　To　verify　the　effectiveness　of　the　proposed　model,　we　conduct　experiments　on　the　Food101　dataset　and　two　constructed　multi-modal　long　document　datasets.　The　experimental　results　show　that　the　proposed　cross-modal　method　outperforms　the　single-modal　text　methods　and　defeats　the　state-of-the-art　related　multi-modal　baselines.　Copyright　©　2024　held　by　the　owner/author(s)