Abstract: 【 Purpose/significance】In the era of Open Science in which everything is interconnected, linking scientific data and scien⁃ tific literature has become an important measure to promote the open access, acquisition, sharing and reuse of scientific data.【 Method/ process】In order to open up a solution path of identifying and extracting the hidden linkage between scientific data and scientific lit⁃ erature, this paper constructs the linkage recognition model between scientific data and scientific literature based on labeled-LDA model and rule-based recognition method. Taking biomedical papers and scientific data as the research object, this paper carries out model training and testing for the three association cases of standardized citation, non-standardized citation and no citation through text mining.【 Result/conclusion】The results show that the F value of the model is about 0.5 when identifying the standardized refer⁃ ence test set, which has a relatively stable recognition effect. When identifying the non-standardized reference test set and the nonreferenced test set, the recognition rates are 0.465 and 0.5 respectively, showing strong portability and great application potential. Through the manual judgment of the recognition results of non- standardized references and non-references, it is found that there is in⁃ deed the phenomenon of non-standard data references in scientific research, which needs the academic community to jointly promote the standardization of data references. 【Innovation/limitation】Compared with other studies, the model constructed in this paper pro⁃ vides a methodological reference and basis for semantic based association recognition, and can be applied to large-scale corpus re⁃ search, so as to promote the knowledge discovery of deeper semantic association.