Abstract:  Automatic scoring of essays is one of the important research directions in the field of smart education. It has the advantages of improving scoring efficiency， reducing labor costs， and ensuring the objectivity and consistency of scoring， so it has broad application prospects in the field of education. Although syntactic features play an important role in automatic scoring of compositions， there is still a lack of research on how to better utilize these features for automatic scoring of compositions. This paper proposes an automatic essay scoring method GFTB based on GCN and fine-tuned BERT. This model uses graph convolutional network to extract syntactic features of compositions， uses BERT and Adapter training methods to extract deep semantic features of compositions， and uses a gating mechanism to further capture the semantic features after the fusion of the two. The experimental results show that the proposed GFTB model achieves good average performance on 8 subsets of the public data set ASAP. Compared with baseline models such as Tongyi Qianwen， the proposed method can effectively improve the performance of automatic essay scoring.

Key words:  , automatic essays scoring； graph neural network； fine-tuning BERT； feature fusion