Aiming at the problems of complex feature extraction and low detection rate in sea-surface small target detection under the background of sea clutter, the data characteristics of sea clutter and target echoes are analyzed, and the applicability of feature mode decomposition (FMD) in sea clutter signal processing is studied. Based on this, a sea-surface small target detection method combining optimized feature mode decomposition and spectral entropy features is proposed. A hybrid intelligent algorithm combining symbiotic organism search (SOS) and particle swarm optimization (PSO) was used for parameter optimization, and multi-scale envelope spectrum entropy (MSESEn) was used to extract signal features. A deep extreme learning machine (DELM) classifier model with controllable false alarm is constructed. The normalized feature data is input into the model, and the decision threshold is updated in real time by comparing the predicted value and the decision threshold. The false alarm rate of the control model is realized, and the reliability and detection efficiency of the algorithm are improved. The IPIX data set is used for verification, and the detection rate is improved by 18% on average under HV polarization mode, which shows that the performance of the proposed method is better than that of Fourier Transform and three-feature detection method.