手把手掌握学术表达常用句式实操指南

作为大学生、研究生或科研人员，你是否曾遇到过这些场景：

• 实验数据很扎实，但写论文时总觉得“语言干巴巴”，达不到期刊要求的“学术感”；
• 想引用前人研究，却只会用“XXX said”，不知道如何自然衔接文献与自己的观点；
• 讨论部分想升华结论，却找不到合适的句式表达“局限性”或“未来展望”；
• 明明逻辑没问题，却被导师批注“表达不严谨”“缺乏学术规范”。

学术写作的核心不是“炫技”，而是“精准传递信息”——而“精准”的前提，是掌握一套符合学术语境的“表达工具箱”。这套工具不需要你从零创造，而是可以通过模仿、练习和内化常用句式，快速提升写作效率和质量。

为了帮你快速定位需求，我整理了学术写作中最常见的5大场景及对应的核心句式方向，你可以先对照下表自查：

接下来，我将用“场景+句式模板+实操练习+AI辅助工具”的步骤式结构，带你手把手掌握这些核心句式——从“知道”到“会用”，再到“用得好”。

在开始练习前，你需要先建立一个“可复用的句式库”。这一步的核心是“分类存储”，避免用的时候“大海捞针”。下面我会带你完成3个操作：明确分类维度、整理核心句式、用工具高效管理。

学术句式的分类不能只看“场景”，还要结合“功能”和“语气”——这样你在写作时才能根据需求快速匹配。具体维度如下：

• 场景维度：对应论文的不同部分（如引言、方法、结果等，参考上表）；
• 功能维度：句式的核心作用（如“引用文献”“提出假设”“对比结果”等）；
• 语气维度：表达的严谨程度（如“确定型”“推测型”“不确定型”）。

举个例子：同样是“引用文献”，你可以用确定型（“XXX (2023) 证实了…”）、推测型（“XXX (2023) 认为可能…”）或对比型（“与XXX (2023) 的研究不同…”）——不同语气传递的信息强度完全不同。

我将学术写作中最常用的句式按场景整理成模板，你可以直接复制到自己的素材库中。每个模板都标注了功能和语气，方便你灵活调整。

文献综述的核心是“总结前人+指出不足+引出自己”，以下是3类高频句式：

实操练习1：假设你研究“人工智能在医学影像中的应用”，请用上述模板写1句话：

Recent studies have focused on the application of AI in medical image diagnosis (e.g., Li et al., 2022; Wang, 2023), which has revealed that deep learning models can improve detection accuracy. However, few studies have addressed the generalization of these models to rare disease datasets, particularly in the context of rural hospitals. To fill this gap, the present study aims to develop a lightweight AI model by integrating transfer learning and data augmentation.

方法部分的核心是“可重复性”——必须让读者能照着你的描述复现实验。以下是4类关键句式：

实操练习2：假设你做“大学生焦虑水平与睡眠质量的相关性研究”，请用上述模板写方法部分的核心句：

The study was designed as a cross-sectional survey with 300 undergraduate students recruited from three universities in Beijing. Data were collected using the Self-Rating Anxiety Scale (SAS) and the Pittsburgh Sleep Quality Index (PSQI), both of which have been widely validated in Chinese college students (Zhang et al., 2019). Quantitative data were analyzed with SPSS 26.0 using Pearson correlation analysis and multiple linear regression, while open-ended responses were coded thematically following the framework of Braun and Clarke (2006). The study was approved by the Institutional Review Board of Peking University (Approval No. 20240315), and all participants provided written informed consent before data collection.

结果部分的核心是“客观描述+突出重点”——不要加入主观解释，只需用数据说话。以下是3类常用句式：

实操练习3：假设你的实验结果是“运动组的焦虑得分显著低于对照组（t=3.25, p<0.001）”，请用上述模板扩展成一段：

Descriptive statistics showed that the anxiety score of the exercise group had a mean value of 45.2 (SD = 6.8), while the control group had a mean of 52.7 (SD = 7.2). A independent-samples t-test revealed a significant difference between the two groups, t(298) = 3.25, p < 0.001. As shown in Figure 2, the exercise group’s anxiety score decreased by 12.3% after 8 weeks of intervention, whereas the control group showed no significant change (p > 0.05).

讨论部分是“体现深度的关键”——需要把你的结果和前人研究结合，解释“为什么会这样”，并承认局限性。以下是4类核心句式：

实操练习4：假设你的结果是“轻量级AI模型在罕见病影像诊断中的准确率达89%”，请用上述模板写讨论部分：

The observed high accuracy (89%) of the lightweight AI model may be attributed to the use of transfer learning, as previous studies have shown that this approach can effectively reduce the need for large labeled datasets (Chen et al., 2023). Alternatively, the data augmentation technique we employed (e.g., rotation, flipping) could also have improved the model’s generalization ability. Our findings are consistent with those of Li et al. (2022), who reported that lightweight models perform well in resource-constrained settings. However, they contradict the study by Zhang (2021), which found that lightweight models had lower accuracy than large models—this discrepancy may be due to the different datasets used (our dataset included more diverse rare disease cases). This study extends previous research by focusing on rare disease diagnosis, and provides new evidence for the application of lightweight AI in rural hospitals, which has important implications for improving medical access in underserved areas. It is important to acknowledge the limitations of this study. First, the dataset was collected from only three hospitals, which may limit the generalizability of the results. Second, we did not test the model in real clinical settings. These limitations suggest that future research should expand the dataset to more hospitals and conduct prospective clinical trials.

结论部分的核心是“简洁总结+未来方向”——不要重复结果，要提炼核心贡献。以下是2类常用句式：

实操练习5：结合前面的例子，用上述模板写结论部分：

In conclusion, this study developed a lightweight AI model for rare disease medical image diagnosis with an accuracy of 89%, and demonstrates that transfer learning and data augmentation can effectively improve the performance of AI models in resource-constrained settings. These findings provide valuable insights into addressing the gap in rare disease diagnosis in rural areas. Future research should expand the dataset to include more rare disease types and hospitals, and explore the integration of clinical data (e.g., patient history) into the model. Additionally, conducting prospective clinical trials in rural hospitals would help to further validate the practical application of the model.

整理好句式后，你需要一个工具来快速检索和更新。这里推荐2个实用工具：

• 操作步骤：

1. 打开Notion，新建一个“数据库”页面；

2. 添加列：场景、功能、语气、句式模板、示例、来源、标签；

3. 将上一节的句式模板逐一填入，并用“标签”标注关键词（如“文献引用”“局限性”）；

4. 保存为模板，以后每次写作时直接搜索标签即可。

• 小技巧：可以用Notion的“关联”功能，把句式和对应的论文段落链接起来，方便参考上下文。

如果你不想手动整理，可以用AI快速生成特定场景的句式。

• Prompt示例：

请你作为学术写作助教，为“讨论部分-与前人研究对比”场景生成5个不同语气的句式模板，要求包含“一致”“矛盾”“补充”三种情况，并标注功能和语气。

• 导出技巧：将AI生成的句式复制到Notion或Excel中，按之前的分类维度整理，避免重复。

掌握句式的关键不是“背下来”，而是“用起来”。这一步我会带你做3个层次的练习：机械套用→灵活变形→个性化调整，帮你从“模板使用者”变成“句式创造者”。

机械套用是入门的第一步，核心是“严格按照模板填空”，确保学术表达的规范性。

练习任务：假设你研究“线上学习对大学生自主学习能力的影响”，请用场景1（文献综述）和场景3（结果呈现）的模板各写2句话。

• 文献综述模板句：

Recent studies have focused on the impact of online learning on college students’ learning outcomes (e.g., Zhao et al., 2023; Sun, 2022), which has revealed that online learning can improve flexibility but may reduce interaction. However, few studies have addressed the specific effect of online learning on autonomous learning ability, particularly in the context of hybrid learning models.

• 结果呈现模板句：

A paired-samples t-test revealed a significant increase in autonomous learning ability scores after 1 semester of online learning, t(149) = 4.12, p < 0.001. As shown in Table 1, the score increased from 65.3 (SD = 8.2) to 72.5 (SD = 7.6).

机械套用容易显得生硬，你需要学会“调整句式结构”，让表达更自然。常见的变形方法有3种：

原模板：Recent studies have focused on [主题], which has revealed that [发现].

变形后：Focusing on [主题], recent studies have revealed that [发现].

原模板：However, few studies have addressed [问题].

变形后：Nevertheless, limited research has explored [问题].

原模板：This study aims to [目标].

变形后：Against the backdrop of [背景], this study aims to [目标] by using [方法], in order to [意义].

练习任务：将层次1的文献综述句变形，使其更自然：

Focusing on the impact of online learning on college students’ learning outcomes, recent studies (e.g., Zhao et al., 2023; Sun, 2022) have revealed that online learning can enhance learning flexibility but may hinder face-to-face interaction. Nevertheless, limited research has explored the specific effect of online learning on autonomous learning ability, especially in the context of hybrid learning models that combine online and offline instruction.

当你熟练掌握变形后，就可以根据自己的研究领域和写作风格，对模板进行“个性化改造”。核心是“保留学术严谨性，增加领域特色”。

示例：如果你是计算机领域的研究者，在描述方法时可以加入领域特定术语：

原模板：Data were analyzed using [软件] and [方法].

个性化后：The dataset was preprocessed using Python’s Pandas library, and the model was trained using PyTorch with a batch size of 32 and a learning rate of 0.001. The performance was evaluated using accuracy, precision, recall, and F1-score.

练习任务：假设你是生物学领域的研究者，研究“某种药物对细胞凋亡的影响”，请个性化调整场景2（研究方法）的模板句：

The experiment was designed as an in vitro study using human hepatocellular carcinoma cells (HepG2), which were cultured in DMEM medium supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin at 37°C in a 5% CO₂ incubator. The cells were treated with different concentrations of the drug (0, 10, 20, 40 μM) for 24 hours, and cell apoptosis was detected using flow cytometry (Annexin V-FITC/PI staining kit, BD Biosciences). The data were analyzed using FlowJo software (version 10.0), and the apoptosis rate was calculated as the percentage of Annexin V-positive cells.

AI工具可以帮你快速优化句式，解决“表达生硬”“用词不当”的问题。这一步我会带你用ChatGPT和Grammarly做2个实操：句式润色和学术性检查。

ChatGPT可以帮你调整句式的流畅度、逻辑衔接和学术性。关键是要给出具体的修改指令，而不是“帮我改改这句话”。

1. 输入原始句子：（你写的句子）

Our study found that the drug can reduce cell apoptosis. This is consistent with previous studies.

2. 添加修改指令：

请你作为SCI期刊编辑，帮我润色这两句话。要求：1. 合并成一句话，增强逻辑衔接；2. 用更学术的词汇替换“found”和“reduce”；3. 增加“可能的机制”暗示，为讨论部分铺垫；4. 保持严谨语气。

3. 生成润色结果：

Our study demonstrates that the drug can attenuate cell apoptosis, which is consistent with the findings of previous research (e.g., Li et al., 2022). This effect may be mediated by the inhibition of the PI3K/Akt signaling pathway, although further mechanistic studies are needed to confirm this hypothesis.

如果你想让润色结果更符合特定期刊的风格，可以加入期刊名称：

请你参考《Nature Communications》的写作风格，润色以下句子…

Grammarly的“学术写作模式”可以帮你检查语法错误“用词准确性”和“学术规范”（如被动语态使用、缩写规范）。

1. 打开Grammarly官网（https://www.grammarly.com/），注册并登录；

2. 点击“新建文档”，选择“学术写作”模式；

3. 粘贴你写的段落（包含句式）；

4. 查看右侧的修改建议，重点关注：

• Clarity（清晰度）：是否有歧义？
• Formality（正式度）：是否使用了口语化词汇？
• Academic Tone（学术语气）：是否过于主观？

5. 点击“接受”修改建议，或根据自己的需求调整。

注意：Grammarly的免费版只能检查基础错误，高级版（Grammarly Premium）可以检查学术性和 plagiarism，建议学生用教育邮箱申请折扣。

AI生成的句式可能存在以下问题，需要你手动检查：

1. 过度使用复杂句式：虽然学术写作需要严谨，但过于复杂的句式会降低可读性。例如：

The fact that the drug can attenuate cell apoptosis, which has been demonstrated in our study, is consistent with the findings of previous research that have focused on the same signaling pathway.

优化后：

Our study demonstrates that the drug can attenuate cell apoptosis, consistent with previous research on the same signaling pathway.

2. 不准确的术语使用：AI可能会混淆领域特定术语，例如把“自主学习能力”翻译成“self-learning ability”（正确应为“autonomous learning ability”）；

3. 缺乏引用意识：AI生成的句子可能没有标注引用，需要你手动添加文献来源。

现在你已经掌握了句式整理、模仿练习和AI优化的方法，接下来我们做一个实战：写一段符合SCI期刊要求的“讨论部分”，并结合前面的步骤进行优化。

假设你研究“miRNA-123对肺癌细胞增殖的影响”，实验结果是“miRNA-123过表达显著抑制肺癌细胞增殖（p<0.01）”，请写一段讨论部分（150-200字）。

Our study found that miRNA-123 overexpression can inhibit lung cancer cell proliferation. This is consistent with previous studies. However, some studies have different results. The reason may be the different cell lines used. Our study has limitations, such as small sample size. Future research should study the mechanism.

Our study demonstrates that miRNA-123 overexpression significantly inhibits the proliferation of lung cancer cells (p < 0.01). This finding is consistent with the results of Wang et al. (2022), who reported that miRNA-123 can suppress tumor growth in breast cancer. However, it contradicts the study by Zhang et al. (2021), which found no significant effect of miRNA-123 on lung cancer cell proliferation. This discrepancy may be due to the different cell lines used (we used A549 cells, while Zhang et al. used H1299 cells). Our study has limitations, such as the small sample size of cell lines. Future research should explore the underlying mechanism of miRNA-123 in lung cancer.

用ChatGPT润色（Prompt：请参考《Cancer Research》的风格润色，增加机制推测和研究贡献）：

Our study demonstrates that miRNA-123 overexpression significantly inhibits the proliferation of A549 lung cancer cells (p < 0.01), which aligns with the findings of Wang et al. (2022) who reported that miRNA-123 exerts a tumor-suppressive role in breast cancer by targeting oncogenes. However, our results contradict those of Zhang et al. (2021), who observed no significant effect of miRNA-123 on H1299 lung cancer cell proliferation. This discrepancy may stem from inherent differences in the genetic backgrounds of the two cell lines—A549 cells harbor KRAS mutations, whereas H1299 cells lack functional p53, which could modulate the response to miRNA-123. Our study extends previous research by focusing on the context-dependent role of miRNA-123 in lung cancer, providing a foundation for further mechanistic studies. Limitations include the use of only one cell line and the lack of in vivo validation; future work should investigate the downstream targets of miRNA-123 and evaluate its therapeutic potential in mouse xenograft models.

检查后发现“context-dependent role”用词准确，“mouse xenograft models”符合学术规范，无需修改。

学术写作是一个持续学习的过程，你的句式库也需要不断更新。这一步我会带你做2个事情：定期补充新句式和总结自己的“高频句式”。

• 从文献中学习：每次读论文时，把你觉得“高级”或“实用”的句式复制到素材库中，并标注来源和场景。例如：

From a mechanistic standpoint, our results suggest that [X] acts by [Y], thereby modulating [Z] (Smith et al., 2024).

• 从导师/编辑反馈中学习：如果导师或编辑修改了你的句式，把修改前后的句子对比记录下来，分析“为什么这样改”。例如：
• 原句：We think this result is important.
• 修改后：This result provides important insights into [领域问题], as it reveals [关键发现].
• 分析：修改后更具体，强调了结果的“贡献”而非“主观判断”。

每个人的研究领域和写作习惯不同，你可以总结自己常用的10-20个“高频句式”，形成独特的写作风格。例如：

• 如果你研究机器学习，高频句式可能是：

The proposed model outperforms state-of-the-art methods by [X%] on the [数据集], achieving an accuracy of [Y%].

• 如果你研究社会学，高频句式可能是：

The qualitative analysis revealed three main themes: [主题1], [主题2], and [主题3], which reflect the experiences of [研究对象].

不会。句式是“表达工具”，就像数学公式一样，属于公共知识。抄袭的核心是“复制他人的观点或数据”，而不是“使用相同的句式结构”。但要注意：不要直接复制文献中的完整句子，需要用自己的话替换内容。

• 定期更新模板，加入新的句式；
• 同一功能用不同语气的句式交替使用；
• 在模板基础上增加个性化细节（如领域术语、实验细节）。

• 多读本领域的英文顶刊论文，模仿其句式；
• 用AI工具润色后，对比自己的句子和AI的句子，学习差异；
• 参加学术写作工作坊或课程（如Coursera的“Academic English Writing”）。

掌握学术表达常用句式不是终点，而是“精准传递研究价值”的手段。通过“整理素材库→模仿练习→AI优化→持续迭代”这四个步骤，你可以快速提升学术写作的效率和质量。

最后给你一个小建议：每天花10分钟做一个句式练习——比如从文献中选一个句式，用自己的研究内容替换，坚持一个月，你会发现学术写作变得“轻松”很多。

现在，打开你的论文文档，试着用今天学的句式修改一段吧！