Feature　modeling　for　complex　industrial　processes　is　the　basis　for　studying　their　optimal　control.　Complex　industrial　processes　generally　have　uncertain　characteristics　such　as　strong　interference,　nonlinearity,　and　large　time-varying.　Some　of　the　processes　involve　complex　biochemical　reactions　with　strong　contamination　and　high　risk,　and　the　detection　data　is　highly　dimensional　and　noisy,　which　all　put　forward　more　urgent　needs　and　higher　standards　for　the　establishment　of　accurate　industrial　models.　This　paper　summarizes　the　current　modeling　ideas　and　research　progress　of　complex　industrial　processes,　and　aims　to　analyze　the　applicability　and　effectiveness　of　different　modeling　methods　from　multiple　perspectives,　so　as　to　lay　the　modeling　foundation　for　the　advanced　optimal　control　theory　to　guide　the　actual　industrial　production.　First,　the　main　industrial　modeling　methods　are　divided　and　summarized　from　three　aspects:　mechanism　modeling,　data-driven　modeling　and　hybrid　modeling.　Second,　the　specific　design　ideas　of　various　modeling　methods　are　described,　and　the　model　structure　and　algorithm　characteristics　are　analyzed.　Then,　the　specific　applications　of　different　modeling　strategies　in　solving　the　problems　of　index　modeling,　controlled　object　modeling,　and　full-scale　modeling　in　the　actual　industrial　processes　are　investigated.　Finally,　combined　with　the　current　trend　of　industrial　intelligent　construction　and　its　challenging　problems,　the　future　research　ideas　and　development　directions　are　pointed　out.　©　2023　Northeast　University.　All　rights　reserved.