认识钢轨服役性能演化规律对降低重载铁路钢轨运维成本具有重要意义。针对钢轨运行环境复杂多变，难以构建科学有效的伤损演化指标以反映客观发展规律的问题，提出了基于t-分布随机近邻分布（t-distributed stochastic neighbor embedding, t-SNE）的波磨伤损演化规律构建方法。首先，对钢轨原始波磨振动信号提取时域、频域、统计学、熵的特征指标；然后，使用随机森林算法对特征进行特征重要性排名，选取排名靠前的特征构建特征矢量；接着使用t-SNE等方式降维，验证了t-SNE更具优势，使用欧氏距离度量和中值滤波法进行平滑处理得到最终的时序性伤损退化指标。结果表明本文方法对于伤损阶段的划分具有较好的区分度、抗干扰能力和工程实用性。

Understanding the evolution law of rail service performance is of great significance for reducing the operation and maintenance costs of heavy-duty railway rails. Due to the complex and variable operating environment of rail tracks, which makes it difficult to construct scientifically effective damage evolution indicators to reflect objective development patterns, a method based on t-SNE(t-distributed stochastic neighbor embedding) was proposed for constructing the evolution law of corrugation damage. Firstly, the time-domain, frequency-domain, statistical, and entropy features were extracted from the original rail corrugation vibration signal. The random forest algorithm was then used to rank the features by importance, and the top-ranked features were selected to construct the feature vector. Dimensionality reduction was performed using t-SNE and other methods, and it is found that t-SNE demonstrates superior performance. The final temporal damage degradation index is obtained through Euclidean distance metric and median filtering for smoothing. The results indicate that this method provides good discrimination, anti-interference capability, and practical applicability for damage stages classification.