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As the number of open and shared scientific datasets on the Internetincreases under the open science movement, efficiently retrieving thesedatasets is a crucial task in information retrieval (IR) research. In recentyears, the development of large models, particularly the pre-training andfine-tuning paradigm, which involves pre-training on large models andfine-tuning on downstream tasks, has provided new solutions for IR match tasks.In this study, we use the original BERT token in the embedding layer, improvethe Sentence-BERT model structure in the model layer by introducing the SimCSEand K-Nearest Neighbors method, and use the cosent loss function in theoptimization phase to optimize the target output. Our experimental results showthat our model outperforms other competing models on both public and self-builtdatasets through comparative experiments and ablation implementations. Thisstudy explores and validates the feasibility and efficiency of pre-trainingtechniques for semantic retrieval of Chinese scientific datasets.

CSDR-BERT: a pre-trained scientific dataset match model for Chinese Scientific Dataset Retrieval