Dioxin　(DXN)　is　a　kind　of　pollutants　with　cumulative　effects　in　the　municipal　solid　waste　incineration　(MSWI)　process.　Its　emission　concentration　is　difficult　to　detect　online　and　in　real-time,　which　restricts　the　operational　optimization　of　the　MSWI　process.　At　the　same　time,　it　is　difficult　to　meet　actual　needs　through　traditional　supervised　modeling　methods　because　of　the　high　time　and　economic　cost　of　directly　measuring　DXN.　Therefore,　a　DXN　emission　prediction　model　based　on　semi-supervised　random　forest　(SSRF)　is　established　to　make　full　use　of　the　unlabeled　data　obtained　in　the　actual　industrial　process.　First,　the　training　subsets　are　acquired　through　randomly　sampling　the　labeled　data.　Second,　the　training　subsets　are　utilized　to　build　multiple　random　forest　(RF)　models　and　pseudo-label　the　unlabeled　data.　Finally,　the　mixed　samples　composed　of　pseudo-labeled　data　and　labeled　data　are　used　to　train　an　RF　model　for　predicting　the　DXN　emission　concentration.　The　proposed　method　is　verified　by　the　actual　DXN　dataset.　©　2021　IEEE.