  As the number of open and shared scientific datasets on the Internet
increases under the open science movement, efficiently retrieving these
datasets is a crucial task in information retrieval (IR) research. In recent
years, the development of large models, particularly the pre-training and
fine-tuning paradigm, which involves pre-training on large models and
fine-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, improve
the Sentence-BERT model structure in the model layer by introducing the SimCSE
and K-Nearest Neighbors method, and use the cosent loss function in the
optimization phase to optimize the target output. Our experimental results show
that our model outperforms other competing models on both public and self-built
datasets through comparative experiments and ablation implementations. This
study explores and validates the feasibility and efficiency of pre-training
techniques for semantic retrieval of Chinese scientific datasets.
