Aiming at the problem of accurate denoising of magnetic field signals under strong noise interference, a new method of denoising magnetic field signals combining the auxiliary sensor array of center-satellite architecture and the deep noise reconstruction network is proposed. First, the magnetic field sensor array is built, and finite element analysis is used to optimize the sensor array positions and analyze the signal characteristics between the center and satellite sensors. Subsequently, a deep neural network model combining noise-enhanced convolutional neural network (NECNN) and bi-directional long short-term memory (BiLSTM) is constructed. The model is trained using the noise signals captured by the sensor array to reveal the nonlinear mapping relationship between the center sensor signal and the satellite sensor signal. Finally, in the magnetic field detection process, the noise components of the center sensor are reconstructed using the noise of the satellite sensor array. The denoised magnetic field signal is obtained by subtracting the reconstructed noise from the noisy signal captured by the center sensor. The experimental results show that the proposed method outperforms the conventional method in terms of the maximum error and the root mean square error index of magnetic field denoising. This new approach provides a new means of dynamic denoising of signals under strong magnetic field interference, and is expected to be applied in the fields of current detection, magnetic field imaging, and battery quality detection.