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High-frequency surface wave radar (HFSWR), as a novel ocean observation radar, enables over-the-horizon, all-weather monitoring of the sea surface, unaffected by the Earth's curvature. Currently, HFSWR is widely applied in maritime target detection and ocean dynamic parameter measurement, playing a crucial role in both military and civilian domains. During operation, strong sea clutter often overwhelms target echoes, significantly affecting maritime target detection accuracy. Effective suppression of sea clutter is therefore key to improving detection performance. This study leverages the chaotic characteristics of sea clutter to establish a prediction model using radial basis function (RBF) neural networks. By predicting sea clutter and subtracting it from radar echoes in the time domain, effective clutter suppression is achieved. Intelligent optimization algorithms are introduced and improved to optimize the RBF network, enhancing prediction accuracy and further suppressing clutter.