Detecting　and　identifying　malignant　nodules　on　chest　computed　tomography　(CT)　plays　an　important　role　in　the　early　diagnosis　and　timely　treatment　of　lung　cancer,　which　can　greatly　reduce　the　number　of　deaths　worldwide.　In　view　of　the　existing　methods　in　pulmonary　nodule　diagnosis,　the　importance　of　clinical　radiological　structured　data　(laboratory　examination,　radiological　data)　is　ignored　for　the　accuracy　judgment　of　patients＇　condition.　Hence,　a　multi-modal　fusion　multi-branch　classification　network　is　constructed　to　detect　and　classify　pulmonary　nodules　in　this　work:　(1)　Radiological　data　of　pulmonary　nodules　are　used　to　construct　structured　features　of　length　9.　(2)　A　multi-branch　fusion-based　effective　attention　mechanism　network　is　designed　for　3D　CT　Patch　unstructured　data,　which　uses　3D　ECA-ResNet　to　dynamically　adjust　the　extracted　features.　In　addition,　feature　maps　with　different　receptive　fields　from　multi-layer　are　fully　fused　to　obtain　representative　multi-scale　unstructured　features.　(3)　Multi-modal　feature　fusion　of　structured　data　and　unstructured　data　is　performed　to　distinguish　benign　and　malignant　nodules.　Numerous　experimental　results　show　that　this　advanced　network　can　effectively　classify　the　benign　and　malignant　pulmonary　nodules　for　clinical　diagnosis,　which　achieves　the　highest　accuracy　(94.89%),　sensitivity　(94.91%),　and　F1-score　(94.65%)　and　lowest　false　positive　rate　(5.55%).