[Objective] To normalize different expressions of the same citation document, realize standard control and management of periodical citation data, and alleviate the data quality problems caused by citation anomie.[Methods] Taking the construction of the periodical citation database as the target scenario, the core characteristics of periodical citation data were analyzed according to the reference standards. The subsets of effective features were obtained based on the decision tree and accuracy, the execution priority of decision rules was specified and an automatic data processing strategy was constructed based on multi-feature fusion.[Results] 10,000 periodical citation sample data and 10,000 validation data sets were selected from the Chinese Biomedical Citation Index (CBMCI) for the experiment. The results show that our proposed feature fusion approach achieved 99.72% and 98.70% accuracy of the journal citation normalization on these two datasets, respectively.[Limitations] This article only explored the Chinese periodical citation anomie data and has not yet covered the citations of other languages and types.[Conclusions] The proposed method could automatically standardize large-scale journal citation data with high efficiency, thus reduce the burden of labor-intensive manual intervention. The idea of feature fusion can be also applied to the automatic normalization strategies of other types of citation documents.