In order to improve the detection ability of weak ship shaft-rate electric field in the background of marine environment electric field, the ALE (adaptive line enhancement) based on incremental meta-learning IDBD (incremental delta-bar-delta) algorithm was proposed to improve the traditional LMS (least mean square) algorithm. The proposed algorithm was used to process the measured shaft-rate electric field signal data generated by the ship scale model. The results show that the algorithm can effectively separate the weak shaft-rate electric field signal from the broadband background noise under the condition of low SNR(signal-to-noise ratio). Compared with the ordinary ALE algorithm, the proposed algorithm has a more significant effect in improving the SNR of the signal, and has a faster convergence speed and a smaller steady-state error, which greatly improves the ability to test shaft-rate electric field of the ship.