Traditional　breast　cancer　image　classification　methods　require　manual　extraction　of　features　from　medical　images,　which　not　only　require　professional　medical　knowledge,　but　also　have　problems　such　as　time-consuming　and　labor-intensive　and　difficulty　in　extracting　high-quality　features.　Therefore,　the　paper　proposes　a　computer-based　feature　fusion　Convolutional　neural　network　breast　cancer　image　classification　and　detection　method.　The　paper　pre-trains　two　convolutional　neural　networks　with　different　structures,　and　then　uses　the　convolutional　neural　network　to　automatically　extract　the　characteristics　of　features,　fuse　the　features　extracted　from　the　two　structures,　and　finally　use　the　classifier　classifies　the　fused　features.　The　experimental　results　show　that　the　accuracy　of　this　method　in　the　classification　of　breast　cancer　image　data　sets　is　89%,　and　the　classification　accuracy　of　breast　cancer　images　is　significantly　improved　compared　with　traditional　methods.　©　2021　IEEE.