SO2　emissions　are　known　to　pose　great　harm　to　both　human　health　and　atmospheric　air,　and　flue　gas　generated　from　coal-fueled　power　plant　is　the　prime　source　of　sulfur　dioxide.　For　this　reason,　flue　gas　desulfurization　(FGD)　technology　has　found　wide　applications　in　most　coal-fired　power　stations.　Correctly　describe　the　dynamic　behavior　of　an　FGD　process　is　the　precondition　of　controlling　it　effectively.　However,　FGD　process　modeling　is　by　no　means　an　easy　task,　as　the　underlying　process　dynamics　are　highly　nonlinear　in　nature,　meanwhile　time-delay　effect　is　significant　therein.　Long　short-term　memory　(LSTM)　network　possesses　remarkable　long-term　memory　capability,　hence　it　is　anticipated　to　have　a　powerful　identification　capability.　In　this　paper,　the　connection　between　deep　learning　and　system　identification　is　established,　further　a　unidirectional/bidirectional　LSTM　deep　network　is　designed　and　employed　to　identify　a　real　FGD　process.　Simulation　results　clearly　demonstrate　the　effectiveness　of　deep　learning-based　identification　approach,　and　the　superiority　of　deep　LSTMs　over　other　conventional　identification　models　is　also　verified.