Underwater　images　often　suffer　from　color　distortion,　blurred　details,　and　low　contrast.　Therefore,　more　researchers　are　exploring　underwater　image　enhancement　(UIE)　methods.　However,　UIE　models　based　on　deep　learning　suffer　from　high　computational　complexity,　thus　limiting　their　integration　into　underwater　devices.　In　this　work,　we　propose　a　lightweight　UIE　network　based　on　knowledge　distillation　(UKD-Net),　which　includes　a　teacher　network　(T-Net)　and　a　student　network　(S-Net).　T-Net　uses　our　designed　multi-scale　fusion　block　and　parallel　attention　block　to　achieve　excellent　performance.　We　utilize　knowledge　distillation　technology　to　transfer　the　rich　knowledge　of　the　T-Net　onto　a　deployable　S-Net.　Additionally,　S-Net　employs　blueprint　separable　convolutions　and　multistage　distillation　block　to　reduce　parameter　count　and　computational　complexity.　Results　demonstrate　that　our　UKD-Net　successfully　achieves　a　lightweight　model　design　while　maintaining　superior　enhanced　performance.