In view of the wide application of time series data in various fields， the mining and representation of identifiable features of the data is crucial. Due to the influence of the data acquisition environment and acquisition equipment， time series data in many application fields are characterized by high noise， which puts forward high requirements for the robustness of data representation methods. Therefore， a Robust Shapelet representation method for Time series （TRS） was proposed， which adopts the feature extraction method of Key-Shapelet （KS）， retains the interpretability while reducing the influence of noise， and represents the time series by position distance measurement， thereby improving the robustness of the whole method. Experimental results on noise-disturbed time series data show that the features extracted by TRS are significantly better than those of the existing methods in classification， and the average accuracy of TRS is 2.1 percentage points higher than that of the deep learning model — Adversarial Dynamic Shapelet Network （ADSN）， which also extracts features based on Shapelets. It can be seen that the feature set extracted by TRS is more representative and robust.