Abstract: For purpose of solving the problem that a single model of high-speed train wheel tread wear cannot be used for quantitative calculation of train wheel tread wear under various complicated working conditions, we propose a feasible measurement method of wheel tread wear of high-speed trains based on the multilayer extreme learning machine with identity mapping. Firstly, we introduce the identity mapping into the multilayer extreme learning machine, then we propose an identity multilayer extreme learning machine (I-ML-ELM) model. In order to test the I-ML-ELM validity, it is applied to four multivariate regression data sets which are from machine learning public data sets. And experimental results show that the I-ML-ELM can achieve a perfect generalization performance and stability at a fast training speed and a quick reaction of the trained network to new observations. Secondly, based on the vehicle-track coupled dynamics theory, we establish the vehicle-track coupling dynamics model of high-speed trains. By simulating different working conditions, we observe the wheel tread wear of high-speed trains, and the I-ML-ELM prediction model is used to learn and predict the wheel tread wear of high-speed trains. Finally, in order to further test the effectiveness of I-ML-ELM prediction model, it is applied to the actual measurement value of wheel tread wear of high-speed train. The results show that compared with those five learning machines (extreme learning machine, fast learning machine, multilayer extreme learning machine, multilayer kernel extreme learning machine and derived least square fast learning network), the performance parameters of I-ML-ELM prediction model are the best as a whole and the model achieves a very good prediction precision and generalization ability. The further verification of the measured data of high-speed train lines show that the prediction model based on I-ML-ELM can not only reflect the influence of different operating parameters on the wheel tread wear value of high-speed trains better, but also realize the prediction of wheel tread wear.