Abstract: 【Purpose/significance】 Constructing a sentence synonym recognition model combining machine translation and BERTWhitening can improve the effect of synonym sentence recognition and provide support for downstream information resource manage⁃ ment and service applications.【 Method/process】 Firstly, the types and characteristics of synonymous sentences are analyzed, and on this basis, a synonymous sentence recognition model integrating machine translation and BERT-Whitening is constructed, and the ef⁃ fect of the model is verified through experiments. The recognition model consists of four elements: sentence preprocessing, candidate synonym recognition, embedded text representation and synonym judgment based on similarity fusion.【 Result/conclusion】 The experi⁃ mental results show that the accuracy, recall and F1 of the combined model of machine translation and BERT-Whitening are 0.840, 0.859 and 0.849, respectively, significantly higher than the control Group. 【Innovation/limitation】 Lack of text verification in highly specialized fields, insufficient universality verification, and significant room for improvement in accuracy and recall.