With the rapid development of high-speed railways in China, it is inevitable that some of the lines will have to traverse through the mine goaf ground, and there is little research on whether the “activation” of the foundation of the mine goaf ground occurs under the influence of train loads. In order to provide a safe and reliable basis for the construction of high-speed railway in mine goaf ground, a new classification method of mine goaf ground activation is proposed considering the stability and railway influence. First, the stability evaluation system of the mine goaf site is established with 3 primary indexes and 12 secondary indexes. The 47 groups’ data of the mine goaf ground site are collected as learning samples. Five machine learning methods including decision tree, discriminant analysis, support vector machine, and classifier ensemble are used to learn and test the data. The optimal algorithm is selected and the stability evaluation model is established to classify the stability of the mine goaf site. Second, influencing factors of railway are graded to establish an extension comprehensive evaluation model. Finally, based on the above two models, a new classification method of high-speed railway goaf ground activation considering the two factors and five sub-factors is proposed. Through the verification of two engineering examples, the prediction result of this method is “easily activation” and the need to treat the goaf area, and the actual construction is also taken to grouting treatment, proving that the method has certain guiding significance for the project.

With the continuous improvement of infrastructure, some high-speed railway lines will inevitably cross the goaf ground, and there is less research on the safety of high-speed rail construction in goaf ground. To make a reasonable and accurate safety evaluation of the high-speed railway construction in the mine goaf ground, machine learning combined with numerical simulation is used to evaluate the safety depth of goaf under the impact of high-speed railway load. An optimal algorithm is selected among BP, RBF, and PSO-RBF neural networks based on the accuracy of the predicted height of a caving fracture zone. Numerical models for simulating high-speed railway founded on goaf are set up using the commercial software package FLAC3D, where factors such as subgrade height, train speed, and axle load are investigated in terms of train load disturbance depth and the extent of the caving fracture zone. The results indicate that the PSO-RBF neural network has an error of 2.76% in predicting the height of the caving fracture zone; the depth of train load disturbance is linearly related to the axle weight and roadbed height but is a sinusoidal function of the train speed. Based on the numerical simulation results, a formula for calculating the depth of train load disturbance is proposed, which provides a certain reference value for the construction of high-speed railways in the goaf ground.