To address the issue that current time-frequency domain-based speech enhancement methods commonly model the linear characteristics of signals using second-order spectral statistics after Short-Time Fourier Transform （STFT）， while neglecting the potential higher-order nonlinear interaction information in speech， a Bispectrum-based Nonlinear Feature Coupling method for speech enhancement （BNFC） was proposed. An encoder-decoder structure was employed as the overall framework， and a bispectral feature extraction module was introduced after the encoder to capture phase coupling and nonlinear structural information revealed by third-order statistics. By fusing the extracted bispectral features with encoder features through skip connections， deeper amplitude and phase modeling was achieved. Experimental results on the VoiceBank+DEMAND dataset showed that BNFC achieved a Perceptual Evaluation of Speech Quality （PESQ） score of 3.57， representing a 15.53% improvement over the baseline model BREM （Bispectral Refinement Enhancement Module）. In addition， Mean Opinion Score of Signal Distortion （CSIG）， Background Noise Intrusiveness （CBAK）， and Overall Speech Quality （COVL） were improved by 5.51%， 3.08%， and 10.31%， respectively， validating the importance of higher-order nonlinear feature modeling for speech enhancement tasks.