To improve the objectivity， accuracy， and interpretability of device wear evaluation， an improved entropy-weighted TOPSIS method for wear state evaluation is proposed based on multidimensional element data obtained from oil spectroscopy detection， compressively using the DTW， entropy-weighted TOPSIS and system clustering methods. Firstly， the similarity of wear element sequences is measured using the DTW method to extract wear-sensitive features. Secondly， an entropy-weighted TOPSIS wear state evaluation model is established. The contribution weights in wear evaluation are determined based on the information content carried by the wear features and their degree of variation， and the degradation index indicator that quantitatively describes the degree of wear is obtained. Finally， an adaptive hierarchical clustering is performed on the wear degradation indicators to divide the wear state into three stages： normal wear， abnormal wear， and severe wear. A case study on wear evaluation of the power-shift steering transmission is presented， where the DTW distance is used to extract Fe， Cu， and Pb as wear-sensitive features； the wear evaluation weights of Fe， Cu， and Pb are determined to be 0.193， 0.341， and 0.466， respectively， based on entropy calculation； wear degradation index indicator is obtained by the entropy-weighted method to quantitatively describe the wear trend； and the wear state is clustered into three stages using the degradation index， forming a tree-like structure with clear boundaries and strong interpretability. The effectiveness of the improved entropy-weighted TOPSIS method for wear state evaluation is verified， which can provide a scientific basis for health monitoring of comprehensive transmission devices.