## 6 Conclusions  
In this paper, we address the problem that the positive sample construction process in SimCSE may introduce a length bias to the model and propose an Easy Data Augmentation-Based Contrastive Learning of Sentence Embeddings (EdaCSE), which applies a perturbation to the length of the sentence by adding English punctuation to the original sentence, so that the model is no longer inclined to recognise sentences of similar length as similar sentences. Experiments demonstrate that the average performance of the three models on the five datasets is improved by 1.67, 0.84, and 1.08, respectively, after using the EdaCSE method compared with the original model.  
The experiments also explored the number of punctuation symbols inserted and found that the bert-base model can achieve the best performance when inserting 1-3 punctuation symbols, the roberta-large model achieves the best performance when inserting 1-5 symbols, and the roberta-base model can have the best performance when inserting  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **0.1** & **0.2** & **0.3** & **0.4** & **0.6** & **0.8** & **1.0** \\ \hline bert-base & 36.30 & 36.39 & 36.48 & 36.71 & 37.54 & 36.79 & 36.60 \\ roberta-base & 38.23 & 38.38 & 38.73 & 38.42 & 38.14 & 38.39 & 38.21 \\ roberta-large & 38.36 & 38.29 & 38.48 & 38.59 & 39.08 & 38.52 & 38.20 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Selection of the value of the hyperparameter \(\lambda\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Operation** & **ATEC** & **BQ** & **LCQMC** & **PAWS-X** & **STS-B** & **Mean** \\ \hline no-operation & 31.42 & 49.04 & 68.15 & 11.67 & 29.75 & 38.00 \\ EdaCSE & 33.42 & 51.14 & 69.15 & 11.77 & 29.95 & 39.08 \\ Change it to Chinese symbols & 31.74 & 51.42 & 66.72 & 10.63 & 29.48 & 37.99 \\ The Chinese symbol is removed from the “?”, “?” & 31.49 & 51 & 67.23 & 10.98 & 29.33 & 38.00 \\ The English symbol to remove the “?”, “?” & 33.93 & 51.29 & 66.04 & 11.3 & 29.37 & 38.38 \\ Add additional English symbols & 33.23 & 50.15 & 68.53 & 11.49 & 29.99 & 38.67 \\ \hline \hline \end{tabular}
\end{table}
Table 14: An exploration of Chinese and English punctuation-related issues based on the roberta-large model.  
1-2 symbols, with varying degrees of improvement in performance over the baseline model (SimCSE). In addition, through the experimental analysis of Chinese and English punctuation and symbol selection, it was found that the introduction of English punctuation can improve the performance of the Bert-base, roberta-base, and roberta-large models, and the symbols with a strong tone, such as the English exclamation mark and the English question mark, do not have a great impact on the semantics of the Chinese sentences, whereas the model's performance is weaker with the introduction of Chinese punctuation than that of the introduction of English punctuation. Finally, based on the optimal hyperparameter experiments, it was found that introducing the information generated by the EdaCSE algorithm in any proportion will positively affect the original model to varying degrees and enhance the model's performance.  
Therefore, the EdaCSE algorithm proposed in this paper can effectively improve the quality of the model-generated sentence representations, which provides a reference for the research of sentence representation algorithms.  
Conceptualization, W.G.; Methodology, [PERSON] ([PERSON]); Writing--original draft, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); Writing--review & editing, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by research on adaptive integrated evolutionary algorithm for multi-root solution of complex nonlinear equations, grant number 62076225.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.