The　development　of　machine　learning　has　brought　new　methods　for　botnet　detection.　Traditional　machine　learning　methods　and　deep　learning　methods　are　used　in　botnet　detection,　but　the　former　requires　prior　knowledge　to　select　features.　And　deep　learning　methods　solve　this　problem.　This　paper　proposes　a　new　botnet　detection　model　that　combines　Convolutional　Neural　Network　(CNN)　with　Support　Vector　Machine　(SVM).　This　approach　directly　acquires　network　traffic　data,　preprocesses　the　data　to　obtain　input　suitable　for　CNN,　utilizes　CNN　for　feature　extraction　after　preprocessing,　and　feeds　the　extracted　features　through　two　convolutional　layers.　The　obtained　features　are　then　input　to　a　SVM　for　classification.　This　method　leverages　the　powerful　feature　extraction　capabilities　of　CNN　and　the　faster　computational　speed　of　linear　SVM,　resulting　in　faster　training　and　excellent　classification　performance.　Experiments　show　that　the　method　has　good　performance　on　botnet　detection　and　reduces　training　time　compared　to　the　CNN　model.　©　2023　ACM.