The complex and dynamic nature of the marine environment poses significant challenges for sea surface target detection. Traditional methods relying on single-domain features suffer from performance degradation under varying conditions. To address this limitation, a multi-domain polarization-aware feature fusion network capable of controlling the false alarm rate (MP-FFN) for robust sea surface target detection is proposed in this paper. The proposed method first extracts discriminative radar echo features from time, frequency, fractal, and polarization domains. Subsequently, autoencoder-based intra-domain network is employed to reduce feature dimensionality while minimizing information loss. These compressed features are then fused through a multi-layer perceptron (MLP)-based inter-domain network, enabling comprehensive cross-domain correlation learning. Moreover, a controllable false alarm rate is achieved through a customized loss function. Extensive experiments on the IPIX radar dataset demonstrate that the proposed method outperforms traditional feature-based detection methods, exhibiting superior robustness and detection accuracy in diverse marine environments.