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It is difficult to extract deep semantic features for English composition scoring methods based on artificial features, and it is difficult for English composition scoring methods based on neural networks to extract shallow features such as the number of words, resulting in the limitations of different composition scoring methods. Based on existing research results, this paper proposes an English composition scoring method that combines artificial feature extraction methods and deep learning methods. This method uses artificially designed features to extract shallow features at the word and sentence levels in the composition, draws on existing methods to extract semantic features of the composition, and performs regression calculations on the deep features and shallow features to obtain the total score of the composition. The experiment uses the Pearson evaluation index to measure the correlation between the predicted total score of the essay and the true total score under the combination method. The experiment shows that compared with the average results of 0.747 and 0.645 of baseline models such as BiLSTM and RNN, the algorithm proposed in this article is respectively improvements are 0.068 and 0.17, which proves the effectiveness of the method proposed in this paper.
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