Moving window smoothing ensemble CARS (MWS-ECARS) is a stable algorithm for extracting characteristic variables. Based on the previous studies, two improved MWS-ECARS are proposed to reduce the dimension of black tea spectrum based on different window smoothing algorithms in this paper, and compared with the original MWS-ECARS, the commonly used successive projections algorithm (SPA), the competitive adaptive reweighting algorithm (CARS) and the moving window partial least squares method (MWPLS). A partial least square regression model (PLSR) was established to select the best black tea grade discrimination model. Two improved MWS-ECARS methods are Gaussian filter ECARS (GF-ECARS) and Median filter smoothing ECARS (MF-ECARS), respectively. The CARS algorithm runs n times (n=1 000 in this paper). The wavelength and its corresponding selected frequency are sorted out and different window smoothing algorithms are used to smooth the selection frequency. The window widths are all 3~31, and the window step sizes are all 2. The threshold is set through the selection frequency smoothed by different window widths and smoothing algorithm, and the starting threshold and step size are both 20. Finally, the wavelength whose selection frequency is higher than the threshold is selected and the PLSR model is established. The correlation coefficient of prediction set ($R_{\mathrm{p}}^2$) is taken as the judgment factor. The closer $R_{\mathrm{p}}^2$ is to 1, the more accurate the established model is. The results show that the black tea grade discrimination model established by the extracted characteristic variables with the improved GF-ECARS algorithm is the best. The $R_{\mathrm{p}}^2$ reaches 0.969 2. The reason is that the amplitude difference of each point on the curve will become smaller in the window Gaussian filtering smoothing algorithm as the window width increases. In the weighted average process of Gaussian algorithm, it is not easy to associate the low frequency wavelength with the high weight. In practical applications, the selection frequency of effective band is often low, which can be smoothed by selecting a Gaussian filter with narrow window width. In addition, due to the characteristics of the Gaussian curve, the Gaussian filtering algorithm can well protect the details of the window edge image. Although the modeling result of the MF-ECARS algorithm is slightly worse than the original MWS-ECARS, its $R_{\mathrm{p}}^2$ still reaches over 0.96. This shows that the improved algorithm can improve the prediction ability of the original model. MWS-ECARS extraction feature variables are different based on different window smoothing algorithms. However, as the smoothing window width increases, the continuity of the extracted characteristic variables is enhanced and the number of extracted characteristic variables is reduced. The $R_{\mathrm{p}}^2$ of the three MWS-ECARS algorithms all show that they are more effective and stable than the commonly SPA, CARS and MWPRS algorithms. This study can provide ideas for selective dimensionality reduction of spectral data.