Model　recognition　of　second-hand　mobile　phones　has　been　considered　as　an　essential　process　to　improve　the　efficiency　of　phone　recycling.　However,　due　to　the　diversity　of　mobile　phone　appearances,　it　is　difficult　to　realize　accurate　recognition.　To　solve　this　problem,　a　mobile　phone　recognition　method　based　on　bilinear-convolutional　neural　network　(B-CNN)　is　proposed　in　this　paper.　First,　a　feature　extraction　model,　based　on　B-CNN,　is　designed　to　adaptively　extract　local　features　from　the　images　of　secondhand　mobile　phones.　Second,　a　joint　loss　function,　constructed　by　center　distance　and　softmax,　is　developed　to　reduce　the　interclass　feature　distance　during　the　training　process.　Third,　a　parameter　downscaling　method,　derived　from　the　kernel　discriminant　analysis　algorithm,　is　introduced　to　eliminate　redundant　features　in　B-CNN.　Finally,　the　experimental　results　demonstrate　that　the　B-CNN　method　can　achieve　higher　accuracy　than　some　existing　methods.