To　solve　the　problem　that　the　samples　to　construct　a　risk　warning　model　of　dioxin　(DXN)　emission　in　municipal　solid　waste　incineration　(MSWI)　process　are　extremely　scarce,　a　modeling　method　of　DXN　emission　risk　warning　based　on　generative　adversarial　network　(GAN)　with　active　learning　mechanism　was　proposed.　First,　the　risk　level　of　DXN　was　added　as　condition　information　to　GAN,　so　that　the　generator　generated　candidate　virtual　samples　with　specified　requirement.　Then,　the　active　learning　mechanism　based　on　maximum　mean　discrepancy　and　multi-view　visual　distribution　information　was　used　to　evaluate　and　screen　the　virtual　samples　that　met　the　experts＇　expectations.　Finally,　the　DXN　emission　risk　warning　model　was　constructed　based　on　the　mixed　samples　composed　of　virtual　samples　and　real　samples.　The　validity　and　rationality　of　the　proposed　method　were　verified　by　using　benchmark　and　MSWI　process　data　sets.　The　proposed　modeling　method　of　DXN　emission　risk　warning　based　on　GAN　with　active　learning　mechanism　can　effectively　solve　the　problem　of　scarce　samples　and　improve　the　accuracy　of　the　model.　©　2023　Beijing　University　of　Technology.　All　rights　reserved.