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Previous research on the extraction of winter wheat distribution information has mostly relied on single phenological periods or individual vegetation indices, neglecting the characteristics of different phenological periods and their interconnections, which has limited the classification accuracy. To enhance the extraction accuracy, a method for winter wheat identification was proposed based on corresponding feature indices for the sowing period, overwintering period, growth period, and maturation period. The method was applied to extract the winter wheat area in Jiaozuo City in 2020. By comparing the results under different phenological periods and classification methods, the findings indicated that the inclusion of the overwintering period led to varying degrees of improvement in overall accuracy and Kappa coefficients for both random forest and support vector machine classification methods, with respective reductions in root mean square error (RMSE) by 19.3% and 9.8%. The error percentage in winter wheat area extraction was reduced by 8.64 percentage points and 4.42 percentage points, respectively. Among different classification methods, random forest outperforms support vector machine and minimum distance in terms of overall accuracy and Kappa coefficient. Compared with support vector machine, random forest classification reduced RMSE by 19.6%. When compared with single feature indices, the overall accuracy and Kappa coefficient of the multi-phenological feature index based on random forest were higher, with RMSE of 1.84×103hm2, representing 33.6% reduction compared with single feature indices and 7.14 percentage points decrease in the error percentage for winter wheat area extraction.