Since　the　impressive　superior　performance　demonstrated　by　deep　learning　methods　is　widely　used　in　histopathological　image　analysis　and　diagnosis,　existing　work　cannot　fully　extract　the　information　in　the　breast　cancer　images　due　to　the　limited　high　resolution　of　histopathological　images.　In　this　study,　we　construct　a　novel　intermediate　layer　structure　that　fully　extracts　feature　information　and　name　it　DMBANet,　which　can　extract　as　much　feature　information　as　possible　from　the　input　image　by　up-dimensioning　the　intermediate　convolutional　layers　to　improve　the　performance　of　the　network.　Furthermore,　we　employ　the　depth-separable　convolution　method　on　the　Spindle　Structure　by　decoupling　the　intermediate　convolutional　layers　and　convolving　them　separately,　to　significantly　reduce　the　number　of　parameters　and　computation　of　the　Spindle　Structure　and　improve　the　overall　network　operation　speed.　We　also　design　the　Spindle　Structure　as　a　multi-branch　model　and　add　different　attention　mechanisms　to　different　branches.　Spindle　Structure　can　effectively　improve　the　performance　of　the　network,　the　branches　with　added　attention　can　extract　richer　and　more　focused　feature　information,　and　the　branch　with　residual　connections　can　minimize　the　degradation　phenomenon　in　our　network　and　speed　up　network　optimization.　The　comprehensive　experiment　shows　the　superior　performance　of　DMBANet　compared　to　the　state-of-the-art　method,　achieving　about　98%　classification　accuracy,　which　is　better　than　existing　methods.　The　code　is　available　at　https://github.com/Nagi-Dr/DMBANet-main.