Brain　Computed　Tomography　(CT)　report　generation,　which　aims　to　assist　radiologists　in　diagnosing　cerebrovascular　diseases　efficiently,　is　challenging　in　feature　representation　for　dozens　of　images　and　language　descriptions　with　several　sentences.　Existing　report　generation　methods　have　achieved　significant　achievement　based　on　the　encoder-decoder　framework　and　attention　mechanism.　However,　current　research　has　limitations　in　solving　the　many-to-many　alignment　between　the　multi-images　of　Brain　CT　imaging　and　the　multi-sentences　of　Brain　CT　report,　and　fails　to　attend　to　critical　images　and　lesion　areas,　resulting　in　inaccurate　descriptions.　In　this　paper,　we　propose　a　novel　Weakly　Guided　Attention　Model　with　Hierarchical　Interaction,　named　WGAM-HI,　to　improve　Brain　CT　report　generation.　Specifically,　WGAM-HI　conducts　many-to-many　matching　for　multiple　visual　images　and　semantic　sentences　via　a　hierarchical　interaction　framework　with　a　two　-layer　attention　model　and　a　two-layer　report　generator.　In　addition,　two　weakly　guided　mechanisms　are　proposed　to　facilitate　the　attention　model　to　focus　more　on　important　images　and　lesion　areas　under　the　guidance　of　pathological　events　and　Gradient-weighted　Class　Activation　Mapping　(Grad-CAM)　respectively.　The　pathological　event　acts　as　a　bridge　between　the　essential　serial　images　and　the　corresponding　sentence,　and　the　Grad-CAM　bridges　the　lesion　areas　and　pathology　words.　Therefore,　under　the　hierarchical　interaction　with　the　weakly　guided　attention　model,　the　report　generator　generates　more　accurate　words　and　sentences.　Experiments　on　the　Brain　CT　dataset　demonstrate　the　effectiveness　of　WGAM-HI　in　attending　to　important　images　and　lesion　areas　gradually,　and　generating　more　accurate　reports.