With　the　continuous　development　of　recycling　technology　for　urban　mineral　resources‚　recycling　of　used　mobile　phones　has　become　a　hotspot　for　current　research.　Restricted　by　the　relative　lack　of　computing　resources　and　data　resources‚　the　accuracy　of　used　mobile　phone　recognition　based　on　current　off-line　intelligent　recycling　equipment　is　difficult　to　meet　the　practical　application.　Therefore‚　an　image　identification　method　based　on　multivariate　feature　heterogeneous　ensemble　deep　learning　method　was　proposed.　First‚　the　character　region　on　the　back　of　the　mobile　phone　was　extracted　by　using　character　region　awareness　for　text　detection　(CRAFT)　algorithm‚　the　VGG19　model　pre-trained　by　ImageNet　was　used　as　the　image　feature　embedding　model‚　and　the　local　character　feature　and　global　image　feature　were　extracted　by　using　the　transfer　learning　mechanism.　Then‚　the　optical　character　recognition　(OCR)　character　recognition　model　based　on　neural　network　(NN)　mode　was　constructed　by　using　the　local　feature‚　and　the　improved　deep　forest　classification　(DFC)　model　based　on　non-NN　model　was　constructed　by　using　the　global　and　local　features.　Finally‚　the　outputs　of　heterogeneous　OCR　and　the　DFC　model　were　integrated　and　fed　into　the　Softmax　to　ensemble‚　and　the　final　recognition　result　was　obtained　based　on　the　criterion　of　maximum　category　weight　vector.　The　effectiveness　of　the　proposed　method　was　verified　based　on　real　images　of　used　mobile　phone　from　recycling　equipment.　©　2024　Beijing　University　of　Technology.　All　rights　reserved.