Marine current turbines (MCTs) may exhibit reduced energy production and structural instability due to attachments, such as biofouling and plankton. Semantic segmentation (SS) is utilized to recognize these attachments, enabling on-demand maintenance towards optimizing power generation efficiency and minimizing maintenance costs. However, the degree of motion blur might vary according to the MCT rotational speed. The SS methods are not robust against such variations, and the recognition accuracy could be significantly reduced. In order to alleviate this problem, the SS method is proposed based on image entropy weighted spatio-temporal fusion (IEWSTF). The method has two features: (1) A spatio-temporal fusion (STF) mechanism is proposed to learn spatio-temporal (ST) features in adjacent frames while conducting feature fusion, thus reducing the impact of motion blur on feature extraction. (2) An image entropy weighting (IEW) mechanism is proposed to adjust the fusion weights adaptively for better fusion effects. The experimental results demonstrate that the proposed method achieves superior recognition performance with MCT datasets with various rotational speeds and is more robust to rotational speed variations than other methods.