Visual　recognition　technologies　based　on　deep　learning　have　been　gradually　playing　an　important　role　in　various　resource　recovery　fields.　However,　in　the　field　of　metal　resource　recycling,　there　is　still　a　lack　of　intelligent　and　accurate　recognition　of　metallic　products,　which　seriously　hinders　the　operation　of　the　metal　resource　recycling　industry　chain.　In　this　article,　a　convolutional　neural　network　with　dual　attention　mechanism　and　multi-branch　residual　blocks　is　proposed　to　realize　the　recognition　of　metallic　products　with　a　high　accuracy.　First,　a　channel-spatial　dual　attention　mechanism　is　introduced　to　enhance　the　model　sensitivity　on　key　features.　The　model　can　focus　on　key　features　even　when　extracting　features　of　metallic　products　with　too　much　confusing　information.　Second,　a　deep　convolutional　network　with　multi-branch　residual　blocks　as　the　backbone　while　embedding　a　dual-attention　mechanism　module　is　designed　to　satisfy　deeper　and　more　effective　feature　extraction　for　metallic　products　with　complex　characteristic　features.　To　evaluate　the　proposed　model,　a　waste　electrical　and　electronic　equipment　(WEEE)　dataset　containing　9266　images　in　18　categories　and　a　waste　household　metal　appliance　(WHMA)　dataset　containing　11,757　images　in　23　categories　are　built.　The　experimental　results　show　that　the　accuracy　reaches　94.31%　and　95.88%　in　WEEE　and　WHMA,　respectively,　achieving　high　accuracy　and　high　quality　recycling.　©　2022　The　Author(s)