Through adjusting inertia factor and accelerating factors dynamically, the dynamic particle swarm optimization (DPSO) algorithm was designed, so as to keep the dynamic balance between the global and local optimization. Combining DPSO algorithm and the BP neural network, the paper established the dynamic particle swarm optimization neural network (DPSO-NN) model to optimize the network weights for global optimization and locally quadratic optimization respectively. The ice thickness data of Bohai sea was utilized for DPSO-NN model training and predicting. The training and predicting results of the DPSO-NN model were contrasted with the results of BP-NN, GA-NN and PSO-NN models, so as to verify the stability and accuracy of DPSO-NN model.The result can provide more credible environment load parameter for the safety evaluation of ocean platform.