In　wastewater　treatment　processes　(WWTPs),　data　and　knowledge　are　employed　to　build　an　effective　model　for　monitoring　its　operation.　Unfortunately,　they　are　difficult　to　be　fused　due　to　their　heterogeneity,　which　struggles　to　provide　a　united　and　reliable　solution.　To　solve　this　issue,　a　data-knowledge-driven　inductive　learning　(DKIL)　method　is　introduced　to　WWTPs.　First,　a　fuzzy-based　expression　strategy　is　introduced　to　describe　the　operational　status　of　WWTPs.　This　strategy　captures　the　available　data,　constraint　knowledge　and　semantic　knowledge　for　the　modeling　process.　Second,　a　heterogeneous　assimilation　mechanism　is　designed　to　integrate　data　and　knowledge.　This　mechanism　supports　their　interaction　to　form　a　unified　scheme　through　fusion　operations.　Third,　a　collaborative　optimization　algorithm　is　developed　to　extract　the　operational　features　of　WWTPs.　This　algorithm　updates　the　parameters　using　both　error　information　and　semantic　knowledge,　which　enhances　the　modeling　performance.　In　the　experiment,　the　results　have　verified　that　DKIL　can　efficiently　model　WWTPs.