In order to solve the problem of poor adaptivity of the cloth simulation filtering (CSF) algorithm, an improved CSF algorithm (WOA-CSF) based on the whale optimization algorithm (WOA) and adaptive parameter tuning is proposed. In this paper, a fitness evaluation function based on the minimum error rate of misclassified point clouds as the criterion is constructed, then the WOA algorithm is used to adaptively optimize the four parameters of the CSF algorithm, and the WOA-CSF filtering algorithm is constructed, and finally the comparative study of the filtering experiments of the WOA-CSF algorithm and the CSF algorithm is carried out. The experimental results show that the average Kappa coefficient of WOA-CSF algorithm in four complex environments such as cities, towns, villages and mountainous areas improves from 68.33% to 81.54%, the average total error rate decreases from 10.54% to 6.62%, and the average class I error rate decreases from 25.87% to 6.77%. In the complex scene, the non-ground points are well filtered while the terrain features are retained to a great extent.