Establishing　an　accurate　model　of　dynamic　systems　poses　a　challenge　for　complex　industrial　processes.　Due　to　the　ability　to　handle　complex　tasks,　modular　neural　networks　(MNN)　have　been　widely　applied　to　industrial　process　modeling.　However,　the　phenomenon　of　domain　drift　caused　by　operating　conditions　may　lead　to　a　cold　start　of　the　model,　which　affects　the　performance　of　MNN.　For　this　reason,　a　multisource　transfer　learning-based　MNN　(MSTL-MNN)　is　proposed　in　this　study.　First,　the　knowledge-driven　transfer　learning　process　is　performed　with　domain　similarity　evaluation,　knowledge　extraction,　and　fusion,　aiming　to　form　an　initial　subnetwork　in　the　target　domain.　Then,　the　positive　transfer　process　of　effective　knowledge　can　avoid　the　cold　start　problem　of　MNN.　Second,　during　the　data-driven　fine-tuning　process,　a　regularized　self-organizing　long　short-term　memory　algorithm　is　designed　to　fine-tune　the　structure　and　parameters　of　the　initial　subnetwork,　which　can　improve　the　prediction　performance　of　MNN.　Meanwhile,　relevant　theoretical　analysis　is　given　to　ensure　the　feasibility　of　MSTL-MNN.　Finally,　the　effectiveness　of　the　proposed　method　is　confirmed　by　two　benchmark　simulations　and　a　real　industrial　dataset　of　a　municipal　solid　waste　incineration　process.　Experimental　results　demonstrate　the　merits　of　MSTL-MNN　for　industrial　applications.