The echo signals in fiber laser networks are affected by factors such as optical attenuation and dispersion, resulting in complex and diverse characteristics of the echo signals, which reduces the effectiveness of signal enhancement. Therefore, a genetic neural network-based method for enhancing the echo signals in fiber laser networks is proposed. Using empirical mode decomposition method, the original echo signal is decomposed into several intrinsic mode functions, and rough penalty functions are used for smoothing to suppress signal noise. Based on the Gaussian function fitting method, extract features that are consistent with the denoised echo signal function rules. Based on the feature extraction results of the echo signal, genetic algorithm is used to optimize the weights and thresholds of the neural network. Multiple training operations are performed to output the optimal solution sequence, which is the echo signal enhancement result, avoiding the impact of changes in echo signal features on the enhancement result. The experimental results show that this method can effectively suppress noise in the echo signal, accurately extract the waveform change characteristics of the echo signal, and achieve a peak signal-to-noise ratio of up to 42.3 dB.