We propose a multi-stage underwater image enhancement model that can simultaneously fuse spatial details and contextual information. The model is structured in three stages: the first two stages utilize encoder-decoder configurations, and the third entails a parallel attention subnet. This design enables the model to concurrently learn spatial nuances and contextual data. A supervised attention module is incorporated for enhanced feature learning. Furthermore, a cross-stage feature fusion mechanism is designed is used to consolidate the intermediate features from preceding and succeeding subnets. Comparative tests with other underwater enhancement models demonstrate that the proposed model outperforms most extant algorithms in subjective visual quality and objective evaluation metrics. Specifically, on the Test-1 dataset, the proposed model realizes a peak signal-to-noise ratio of 26.2962 dB and structural similarity index of 0.8267.