## 4 Experiment
### Feature Performance Analysis  
To verify the effect of features at different scales on the final essay scores, we perform ablation experiments on each scale module of the model. We use SLF to denote the shallow linguistic feature module; DOC to denote the global semantic module based on document scale; SEN to denote the local semantic module based on sentence scale; DOC-SEN to denote the deep feature module combining global semantics and local semantics; DOC-SEN-SLF to denote the simultaneous use of local, global, and shallow semantic features; and MSSF to denote the paper's proposed essay scoring method based on all features. Table 4 lists the effects of different levels of semantic features on the model; the experimental results show that:  
1. The hybrid DOC-SEN model makes a significant contribution to the overall enhancement of the AES performance. Compared with DOC and SEN, the hybrid model has significantly higher performance improvement on a subset of both, with an overall performance improvement of 2.2% and 1.3%, respectively. The experimental results show that combining document-scale global semantic features and sentence-scale local semantic features can better characterize essays and improve essay scoring performance. 2. Utilizing shallow linguistic features alone is insufficient for essay scoring. However, incorporating shallow linguistic features into the model alongside deep semantic features improves the performance by 2%. Experimental results indicate that since DNN models cannot capture shallow features, such as grammatical errors and linguistic richness, artificial features can effectively complement deep semantic features to improve performance. 3. Prompt relevance has an impact on the performance of the model. Compared with deep and shallow semantic features, the addition of prompt-relevance features improves the scores on all subsets. However, the improvement is small, with an overall performance improvement of 0.5%. The experimental results demonstrate that incorporating essay and prompt relevance features can enhance the performance of essay scoring. However, the current approach yields limited performance improvement for the model. To confirm the validity of the hand-crafted shallow linguistic features, we experimentally verified their validity. Figure 5 shows the heat map of the feature weights of the shallow linguistic features as they perform on each data set, with higher weights implying a greater impact on score prediction. We undertake further analysis and find that the weights of the shallow features varied widely between different groups. We believe there are four main causes of this issue: (a) The process of scoring by scorers not only refers to shallow manual features, such as the number of words and sentences, but means they will pay more attention to the deep semantics of the essay, which leads to inaccurate weights on shallow features. (b) Different scorers focus differently on shallow manual features. For example, some scorers care a lot about the number of sentences and some scorers care a lot about the number of nouns. (c) Even if scorers pay attention to the same shallow features, they will not quantitatively analyze the specific value of each shallow feature during the scoring process, which leads to bias in the final weights. (d) Different essay types and different prompts lead to a different emphasis on shallow feature weights for each essay. Therefore, it is difficult to guarantee the consistency of weights in different essays. In order to verify the influence of the sentence vectorization method on the final score during the extraction of sentence-scale local features, we used different sentence vectorization methods for the essay to the one proposed in this paper. The experimental results are shown in Figure 6, which shows that utilizing Avg. Glove Embedding to obtain