Accurate　models　ensure　the　efficient　and　safe　operations　of　industrial　processes.　However,　modeling　of　industrial　processes　with　non-stationary　characteristic　is　challenging.　In　this　study,　an　interval　type-2　fuzzy　neural　network　(IT2FNN)　based　on　active　semi-supervised　learning　(ASSL-IT2FNN)　is　proposed　for　such　industrial　processes.　First,　an　IT2FNN　with　adaptive　fuzzy　membership　function　(FMF)　and　hierarchical　learning　method　is　utilized　to　achieve　considerable　modeling　accuracy　and　efficiency.　Second,　to　better　tackle　with　the　non-stationary　characteristic,　an　unsupervised　distribution　detection　method　is　proposed　to　identify　the　occurrence　moments　of　concept　drift　actively　from　the　perspective　of　probability　and　spatial　projection.　Then,　by　an　active　semi-supervised　learning　method,　the　concept　drift　samples　with　partial　labels　are　used　to　build　a　subnetwork,　guaranteeing　the　performance　of　the　constructed　IT2FNN　models　with　incomplete　data　in　non-stationary　environments.　Finally,　the　proposed　ASSL-IT2FNN　is　verified　by　benchmark　simulations　and　real　industrial　data,　and　the　experimental　results　demonstrate　its　outperformance.　Note　to　Practitioners-Concept　drift　is　an　important　indicator　of　a　non-stationary　environment.　It　manifests　as　changes　in　the　distribution　of　samples　over　time,　which　may　reduce　the　accuracy　of　models　constructed　based　on　historical　samples.　This　study　aims　to　alleviate　the　impact　of　concept　drift　on　model　performance　in　non-stationary　industrial　processes　while　reduce　the　cost　of　sample　labeling.　By　using　the　adaptive　FMF　and　hierarchical　learning　method,　the　designed　IT2FNN　can　effectively　deal　with　the　inherent　nonlinearity　of　complicated　industrial　processes.　The　samples　representing　concept　drift　are　actively　identified　by　the　Kullback-Leibler　(KL)　divergence　and　maximum　mean　difference　(MMD),　enabling　timely　detections　of　concept　drift　moments　in　unsupervised　situations.　The　active　semi-supervised　learning　is　used　to　adapt　the　constructed　model　to　handle　the　concept　drift,　wherein　a　subnetwork　is　designed　based　on　concept　drift　samples　and　similar　historical　samples.