In view of the flaw of discontinuous or intersecting spatial domains identified by graph neural networks in the clustering process of spatial transcriptome data, a spatial transcriptome clustering method mcmlST, which is based on multi-scale graph contrastive learning, has thus been proposed. Firstly, the spatial transcriptome data is preprocessed by using SCANPY and principal component analysis, followed by an enhancement of the ST data to form a new view. Next, based on graph autoencoders and auxiliary autoencoders, a dual encoding structure is designed to learn the embedded features of spatial transcriptome data. Finally, the k-means algorithm is used for an identification of spatial domains in spatial transcriptome data on the basis of embedded features. On three classic spatial transcriptome datasets (right dorso lateral prefrontal cortex, human breast cancer Block A Section 1 and STARmap), the proposed method calculates higher ARI and NMI compared with the three baseline methods conST, CCST, and DeepST, indicating a superior spatial transcriptome clustering performance.