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Plastic-film-mulching has made an outstanding contribution to agricultural production and food security in China, but also caused many serious environmental problems. It is very important to quickly and accurately obtain the spatial distribution information of plastic-mulched farmlands. In order to establish a framework for remote sensing recognition of plastic-film-mulched farmland, the Tuanjie Town of Dingxi City in Gansu Province was chosen as the research area, which was a typical dry farming agricultural area with heavy plastic film application on the Loess Plateau. Based on the Google Earth Engine, Landsat-8 reflectance data was used to analyze the importance of different features and select the optimal textural features. Then, the random forest (RF) algorithm with optimized parameters was used to extract the plastic-film-mulched farmland area and select the best feature combination. Finally, based on the best feature combination, the performance of RF algorithm was evaluated through comparison between the classification results based on the other algorithms of support vector machines (SVM), decision tree (DT), and minimum distance classifier (MDC), respectively. The results showed that the optimized parameters of RF algorithm could greatly improve the classification accuracy. Among the schemes based on single kind of features, the accuracy of scheme based on spectral features was the highest. The addition of index and textural features could also improve the overall identification accuracy to some extent. The performance of the selected optimal texture features was better than that of all texture features. The recognition result based on the combination of ‘spectral + index + optimal textural features’ was the best, whose overall accuracy and Kappa coefficient were 95.05% and 0.94, respectively. The overall accuracy of RF algorithm was 3.10 percentage points, 7.74 percentage points and 50.78 percentage points higher than the algorithms of SVM, DT and MDC, respectively, which proved the RF algorithm had some obvious advantages in recognition of plasticfilmmulched farmlands. The research realized an accurate identification of plasticfilmmulched farmlands in areas with complex terrain features in China. The results can provide theory and technology supports for the studies related to spatial variations and sustainability production with plastic film mulching in the near future.