Abstract: 【Purpose/significance】To promote the identification of potential "high-quality" literature and its application in the field of identification【. Method/process】This paper takes the articles from journals named Science and Nature,as well as their citation distribu⁃tion data as sample.Such characteristics of each article as first-citation time,abstract length,total citation times,financial support and paper length was calculated to construct the feature matrix of "high-quality" articles. Then, based on the feature matrix of "highquality" articles and random forest algorithm,the recognition model of potential "high-quality" articles is trained and applied【. Result/conclusion】The results show that the fusion of the feature matrix of "high-quality" articles and the random forest model can efficiently identify the potential "high-quality" articles from Science and Nature,and the model's average accuracy of correct recognition and classification is over 80%,among which the accuracy of identifying "high-quality" articles in the Nature was about 2% higher than that
in the Science.The model’s effect of recognition using the information gain method is slightly better than that using the Gini impurity method.In addition,the first citation of "high-quality" articles in the Science and Nature is extremely rapid,being cited within the year of publication【Innovation/limitation】The combination of "high-quality" literature feature matrix and machine learning model can be well applied to the identification and recommendation of potential "high-quality" articles in high-impact journals.However,in the fu⁃ture,the model needs to be popularized and applied to the identification and inspection of "high-quality" articles in massive literature.