Abstract:  Automatic essay scoring is a technology that uses natural language processing technology to automatically evaluate and score essays. Automatic scoring of essays can improve scoring efficiency， reduce labor costs， ensure the objectivity and consistency of scoring， and has broad application prospects in the field of education. Although syntactic features and thematic features play an important role in automatic scoring of essays， so far， there is relatively insufficient research on how to better utilize these features for automatic scoring of essays. This paper proposes an automatic essay scoring method ISSF that integrates syntactic features and semantic features. The model uses Parser to extract the syntactic features of the essay， and uses BERT and adapter training methods to extract the deep semantic features of the essay. In order to better utilize the topic features and for the correlation between syntactic features and deep semantic features， the self-attention mechanism is used to extract thematic features of the essay and used to enhance syntactic features and deep semantic features. Experimental results show that the ISSF model proposed in this paper has achieved better average performance on 8 subsets of the public data set ASAP. Compared with baseline models such as Tongyi Qianwen， the ISSF model has a larger scoring range and complex scoring standards. In this case， it has more performance advantages.

Key words: automatic essay scoring, topic features, syntactic features, deep semantic features