Abstract: SimCSE,as a contrastive learning method,has shown good performance in text embedding and clustering.The aim of this paper is to optimize the sentence embedding generated by SimCSE training models to make them suitable for clustering tasks.By combining multiple algorithms and adjusting training parameters,the problems of clustering algorithm selection,noise,and outliers can be solved.This paper proposes an unsupervised clustering model SimCSE t-SNE KMeans(STK) that combines KL divergence and K-Means algorithm.SimCSE is used to encode the text,and then the t-SNE algorithm is used to reduce the dimensionality of high-dimensional embeddings.By minimizing KL divergence and preserving the similarity relationship between high-dimensional data points in low dimensional space,the dimensionality is reduced while improving the text embedding representation.Finally,the KMeans algorithm is used to cluster the reduced embeddings and obtain clustering results.By comparing the clustering results of this study with those obtained by algorithms such as Bert,UMAP,HDBSCAN,etc.,it is found that the model proposed in the paper showed better clustering performance in the field of hydrogen productionpatent and paper datasets,especially in the evaluation index of Silhouette coefficient.

Key words: SimCSE, Sentence embedding, KL divergence, Clustering, Silhouette coefficient