To reduce the time consumption in the training process with mesh segmentation method based on the deep learning, this paper proposed a mesh segmentation based on optimizing extreme learning machine with ant lion optimization. This paper utilized the dual influence of the elite ant lion and roulette strategy in the ant lion optimization algorithm, iteratively updated the ant colony, sorted the ant lion colony and ant colony in descending order, considered the optimal N to update ant lion colony, and used the optimal ant lion to update the elite ant lion to keep the elite ant lion as the optimal solution. Therefore, the input weight matrix and the hidden layer bias randomly generated by the extreme learning machine were optimized, and a high-precision segmentation classifier was obtained using the improved extreme learning machine method. Considering six models in Princeton Shape Benchmark (PSB) dataset, the results show that on the model dataset such as Airplane, Ant, Chair, Octopus, Teddy, and Fish, the training time of the models with the number of faces ranging 200000-300000 is approximately 1000 s. The proposed method has high segmentation accuracy, with the highest segmentation accuracy being 99.49%.