Abstract As the primary method to deeply understand citation behavior, citation classification research plays an important role in many scenarios, such as document management, retrieval, and utilization. This study uses machine learning methods to further explore citation classification by reviewing important citation behavior mechanisms and citation classification research. In this study, the fields of the original dataset can be supplemented and increased by matching the literature database and document analysis, and the features of four major categories that may be related to citation classification are extracted during the construction of the citation classification model. Thereafter, the feature selection is conducted using a simulated annealing algorithm. The results indicate that the established random forest model has the best performance on citation influence and citation function classification and outperforms the classification model combining the support vector machine with the SciBERT linear layer. The model established by the study improves the performance of automatic classification of multidisciplinary citations and the process of feature extraction and selection in research, as well as the exploration of the relationship between citation categories and some factors that have certain reference values for related research.
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