Abstract: Due to the time-varying nature of marine environmental parameters, there are many challenges in direct measurement of marine environmental parameters. At present, the mainstream method obtaining marine environmental parameters is inversion. Particle filtering is an important matching inversion method based on Bayes' theorem. After fully sampling marine environmental parameters, the posterior probability of environmental parameters can be calculated more accurately, so that better performance of inversion can be achieved. However, the computation complexity of particle filter matching inversion method increases rapidly with the increase of the number of particles. For this reason, a multi-level parallel computation method of the underwater acoustic model is proposed in this paper, which can efficiently map the particle filter algorithm to the hardware of the multi-core cluster machine. Finally, the parallelism performance test of the particle filter algorithm is carried out on the Tianhe Ⅱ supercomputing platform, and the parallelism efficiency of 87.5% is achieved in the single-node multi-core parallelism test. In the multi-node strong scalability test, the number of particles reaches 12 288, nearly 110 times acceleration performance is achieved in 128 computational nodes.