[Objective] We constructed a hierarchical system for papers published by academic journals and proposed submission guidance based on the similarity between articles and journals.[Methods] We studied journals in the field of Library and Information Science and used hierarchical clustering to construct two-layer architecture. Then, we employed SVM, CNN, and RNN to classify these papers. Third, we compared the results of different characteristic combinations, and selected the most suitable algorithm. To optimize the classification results, we combined the journals with similar coverage.[Results] Once the characteristic combinations were more reflective to the article contents, we got the highest accuracy of 81.84%.[Limitations] The data size needs to be expanded.[Conclusions] The deep learning algorithm does a better job in classification than the machine learning algorithm. Combining journals with similar contents improves the classification results.
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