In order to guarantee the long-term stable communication of underwater devices, the improved sparse Bayesian learning algorithm based on Fast Marginal Likelihood Maximization (IFM-SBL) was proposed to estimate underwater acoustic channels with low complexity and high performance. Especially in the case of low signal-to-noise ratio (SNR), the performance of proposed algorithm can be further improved by threshold denoising and DFT transform denoising. Simulation and sea trial results show the output bite error rate (oBER) after channel estimation of FM-SBL is similar to that of EM-SBL, and it has good robustness in both low SNR and fast or slow time-varying channels. The running speed of FM-SBL and IFM-SBLalgorithm is 90% better than that of EM-SBL algorithm, which greatly reduces the estimation time.