Due　to　the　complexity　of　the　e-commerce　system,　a　hybrid　model　for　online-purchasing　behavior　forecasting　is　developed　to　predict　whether　or　not　a　customer　makes　a　purchase　during　the　next　visit　to　the　online　store　based　on　the　previous　behaviors,　i.e.,　online-purchasing　behavior.　The　proposed　model　makes　contributions　to　literature　from　two　perspectives:　(1)　a　classification　model　is　proposed　based　on　the　＂hybrid　modeling＂　concept,　in　which　an　effective　artificial　intelligence　(AI)　technique　of　support　vector　machine　(SVM)　is　employed　for　classification　forecasting　and　further　extended　by　introducing　the　promising　AI　optimization　tool　of　firefly　algorithm　(FA),　to　solve　the　crucial　but　tough　task　of　parameters　selection,　i.e.,　the　FA-based　SVM　model;　(2)　an　appropriate　predictor　set　is　carefully　designed　especially　considering　online　shopping　cart　use　which　was　otherwise　neglected　in　existing　models,　apart　from　other　common　online　behaviors,　e.g.,　clickstream　behavior,　previous　purchase　behavior　and　customer　heterogeneity.　To　verify　the　superiority　of　the　proposed　model,　an　online　furniture　store　is　focused　on　as　study　sample,　and　the　empirical　results　statistically　support　that　the　proposed　FA-based　SVM　model　considering　online　shopping　cart　use　significantly　beat　all　benchmarking　models　(with　other　popular　classification　methods　and/or　different　predictor　sets)　in　terms　of　prediction　accuracy.