Predicting　consumption　behavior　is　very　important　for　adjusting　supplier　production　plans　and　enterprise　marketing　activities.　Conventional　statistical　methods　are　unable　to　accurately　predict　green　consumption　behavior　because　it　is　characterized　by　multivariate　nonlinear　interactions.　The　paper　proposes　an　optimized　fruit　fly　algorithm　(FOA)　and　extreme　learning　machine　(ELM)　model　for　consumption　behavior　prediction.　First,　to　address　the　problem　of　uneven　search　direction　of　FOA　leading　to　insufficient　search　ability　and　low　efficiency,　the　paper　proposes　a　sector　search　mechanism　instead　of　a　random　search　mechanism　to　improve　the　global　search　ability　and　convergence　speed　of　FOA.　Second,　to　address　the　issue　that　the　initial　weights　and　hidden　layer　bias　values　of　the　ELM　are　randomly　generated,　which　affects　the　learning　efficiency　and　generalization　of　the　ELM,　the　paper　uses　an　improved　FOA　to　optimize　the　weights　and　bias　values　of　ELM　for　improving　the　prediction　accuracy.　Taking　the　green　vegetable　consumption　behavior　of　Beijing　residents　as　an　example,　the　results　show　the　optimization　of　the　initial　weight　and　threshold　of　ELM　by　the　GA,　PSO,　FOA,　and　SFOA,　the　prediction　accuracy　of　the　GA-ELM,　PSO-ELM,　FOA-ELM,　and　SFOA-ELM　models　all　surpass　those　of　ELM.　Compared　with　BPNN,　GRNN,　ELM,　GA-ELM,　PSO-ELM,　and　FOA-ELM　models,　the　RMSE　value　of　SFOA-ELM　was　decreased　by　9.45%,　8.40%,　11.89%,　5.84%,　2.22%,　and　2.69%,　respectively.　These　findings　demonstrate　the　effectiveness　of　the　SFOA-ELM　model　in　green　consumption　behavior　prediction　and　provide　new　ideas　for　the　accurate　prediction　of　consumption　behaviors　of　other　green　products　with　similar　characteristics.