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[Purpose/Significance] Abstracts of scientific documents are often composed of sections with specific functions. Using the deep learning method to identify structural functions of abstracts of scientific documents is conducive to the in-depth analysis of the documents. [Method/Process] In this paper, identifying structural functions of abstracts of scientific documents is transformed into a text classification problem, and its structure functions are divided into four categories: "introduction, methods, results, conclusions (IMRC)". Based on the text content and context features of abstract sentences, the classifier is constructed based on deep learning models such as BERT, BERT-BiLSTM, BERT-TextCNN and ERNIE, to automatically identify structural functions of abstracts of scientific documents. [Results/Conclusions] Experiments are carried out on a dataset with 3,130 articles in the field of eHealth. The results show that the scores of indicators for ERNIE are higher than other models. BERT-TextCNN model is better in dealing with short text, while BERT-BiLSTM model is better in handling long sentences. The method proposed in this paper is helpful for the fine-grained functional understanding of scientific literature abstracts, and is of great significance to the in-depth mining of scientific literature and literature based knowledge discovery.