The　promotion　of　the　＇Internet　+　Recycling＇　model　of　waste　electronic　products　has　made　unmanned　and　intelligent　used　mobile　phone　(UMP)　recycling　equipment　become　the　focus　of　attention　in　the　field　of　typical　urban　solid　waste　recycling.　This　papper　takes　used　mobile　phone　recognition　(UMPR)　based　on　recycling　equipment　as　the　research　object.　We　design　and　implement　a　UMPR　method　based　on　parallel　differential　evolution　(PDE)-gradient　feature　deep　forest　(GfDF)　algorithm.　This　method　is　composed　of　the　UMPR　model　and　the　PDE　parameter　optimization　model.　The　mobile　phone　positioning　and　cropping　module　included　in　the　former　is　based　on　the　Faster–RCNN　model　and　crops　the　image　to　obtain　the　effective　information.　The　GfDF　recognition　module　introduces　a　multi-scale　gradient　feature　strategy　to　make　it　easier　to　learn　the　＇location　module＇　for　capturing　information.　The　PDE　parameter　optimization　module　uses　a　parallel　strategy　to　optimize　the　hyperparameters　of　the　GfDF　model　to　improve　the　accuracy　of　the　UMP　identification.　The　experimental　results　show　that　compared　with　deep　models　and　other　machine　learning　models,　this　method　has　performance　advantages　in　recognition　accuracy　and　training　time.　It　can　effectively　improve　the　degree　of　automation　of　recycling　equipment　and　the　efficiency　of　mobile　phone　recycling.　©　2022　South　China　University　of　Technology.　All　rights　reserved.