### Configure Metadata with bjfusetup

Source: https://context7.com/deedive/bjfuthesis/llms.txt

Use the bjfusetup command to define keywords for both Chinese and English abstracts.

```latex
% 在中文摘要环境中设置中文关键词
\begin{abstract}
  论文的摘要是对论文研究内容和成果的高度概括。
  摘要应对论文所研究的问题及其研究目的进行描述，
  对研究方法和过程进行简单介绍，对研究成果和所得结论进行概括。

  % 使用英文逗号分隔关键词，输出时自动转换为中文分号
  \bjfusetup{
    keywords = {关键词1, 关键词2, 关键词3, 关键词4, 关键词5},
  }
\end{abstract}

% 在英文摘要环境中设置英文关键词
\begin{abstract*}
  An abstract of a dissertation is a summary and extraction
  of research work and contributions.

  % 使用 keywords* 设置英文关键词
  \bjfusetup{
    keywords* = {keyword1, keyword2, keyword3, keyword4, keyword5},
  }
\end{abstract*}
```

--------------------------------

### Clone the BJFUThesis Repository

Source: https://github.com/deedive/bjfuthesis/blob/main/README.md

Use this command to download the template repository to your local machine.

```bash
git clone https://github.com/DeeDive/BJFUThesis.git
```

--------------------------------

### Configure Reference Style with natbib and gbt7714

Source: https://context7.com/deedive/bjfuthesis/llms.txt

Configure the reference style using the natbib package and gbt7714 style file for GB/T 7714-2015 compliance. Use \cite command for in-text citations. Ensure \bibfont is set appropriately.

```latex
% In bjfusetup.tex, configure reference style
\usepackage[sort]{natbib}
\renewcommand{\bibfont}{\wuhao \songti}
\setlength{\bibsep}{0pt plus 0.4ex}
\usepackage[sort]{gbt7714}
\bibliographystyle{gbt7714-numerical}

% Cite references in the main text
Deep learning techniques have made significant progress in image recognition \cite{lecun2015deep}.
Convolutional neural networks were first proposed by LeCun et al. \cite{lecun1998gradient},
and have since undergone several improvements \cite{krizhevsky2012imagenet,simonyan2014very,he2016deep}.

% Add the bibliography list at the end of the main document
\phantomsection\addcontentsline{toc}{chapter}{参考文献}
\bibliography{bibdata/refs}
```

--------------------------------

### 创建列表环境

Source: https://context7.com/deedive/bjfuthesis/llms.txt

使用 enumerate 和 itemize 环境创建有序及无序列表，支持自定义标签格式。

```latex
% 有序列表
\begin{enumerate}[labelsep=.5em, leftmargin=0pt, itemindent=3em]
  \item 数据预处理

  \setlength{\parindent}{2em}
  首先对原始数据进行清洗和标准化处理，去除异常值和缺失值。

  \begin{enumerate}[itemindent=2em]
    \item 数据清洗：删除重复记录和无效数据
    \item 特征标准化：将数值特征归一化到[0,1]区间
  \end{enumerate}

  \item 模型训练

  \setlength{\parindent}{2em}
  使用预处理后的数据训练深度学习模型。
\end{enumerate}

% 无序列表
\begin{itemize}
  \item 支持多种数据格式输入
  \item 提供可视化分析工具
  \item 具备自动化报告生成功能
\end{itemize}
```

--------------------------------

### 创建自动布局长文本表格

Source: https://context7.com/deedive/bjfuthesis/llms.txt

使用 tabularx 宏包实现自动列宽分配，X 和 Z 列类型可用于处理长文本。

```latex
\begin{table}[htbp]
  \xiaowu
  \centering
  \begin{threeparttable}
    \bicaption{相关工作对比}{Comparison of Related Works}
    \label{tab:related-work}
    \begin{tabularx}{\textwidth}{lZZ}
      \toprule
      方法     & 优点                                      & 缺点                                  \\
      \midrule
      方法A    & 计算效率高，适合大规模数据处理            & 对噪声数据敏感，需要大量预处理        \\
      方法B    & 鲁棒性强，能够处理缺失数据                & 训练时间长，参数调优困难              \\
      本文方法 & 结合两者优点，平衡效率与鲁棒性            & 模型复杂度相对较高                    \\
      \bottomrule
    \end{tabularx}
  \end{threeparttable}
\end{table}
```

--------------------------------

### English Abstract Environment

Source: https://context7.com/deedive/bjfuthesis/llms.txt

The abstract* environment handles English abstract formatting and keyword output.

```latex
\begin{abstract*}
  An abstract of a dissertation is a summary and extraction of
  research work and contributions. Included in an abstract should
  be description of research topic and research objective, brief
  introduction to methodology and research process, and summary
  of conclusion and contributions of the research.

  An abstract should be characterized by independence and clarity
  and carry identical information with the dissertation. It should
  be such that the general idea and major contributions of the
  dissertation are conveyed without reading the dissertation.

  An abstract should be concise and to the point. Keywords are terms
  used in a dissertation for indexing, reflecting core information.

  \bjfusetup{
    keywords* = {Machine Learning, Deep Neural Network, Image Recognition,
                 Natural Language Processing, Data Mining},
  }
\end{abstract*}
```

--------------------------------

### Define Chapter and Section Headings

Source: https://context7.com/deedive/bjfuthesis/llms.txt

Standard commands for hierarchical document structure.

```latex
\chapter{绪论}
% 章标题：小三号宋体加粗居中

本章介绍研究背景、研究目的与意义、国内外研究现状以及论文结构安排。

\section{研究背景}
% 节标题：四号宋体加粗

随着人工智能技术的快速发展，深度学习在各个领域的应用日益广泛。

\subsection{深度学习的发展历程}
% 小节标题：小四号宋体加粗

深度学习起源于人工神经网络的研究，经历了多次起伏发展。

\subsubsection{卷积神经网络}
% 四级标题：小四号宋体

卷积神经网络（CNN）是深度学习中最重要的模型之一，
特别适用于图像处理任务。
```

--------------------------------

### 创建带附注的表格

Source: https://context7.com/deedive/bjfuthesis/llms.txt

利用 threeparttable 宏包在表格下方添加注释，并通过 tnote 命令进行标注。

```latex
\begin{table}[htbp]
  \xiaowu
  \centering
  \begin{threeparttable}
    \bicaption{实验参数设置}{Experimental Parameter Settings}
    \label{tab:parameters}
    \begin{tabular}{lcc}
      \toprule
      参数名称           & 取值        & 说明                  \\
      \midrule
      学习率\tnote{1}    & 0.001       & Adam优化器            \\
      批次大小           & 32          & GPU显存限制           \\
      训练轮次\tnote{2}  & 100         & 早停策略              \\
      \bottomrule
    \end{tabular}
    \begin{tablenotes}
      \tiny
      \item[1] 学习率采用余弦退火策略动态调整
      \item[2] 当验证集损失连续10轮不下降时提前终止训练
    \end{tablenotes}
  \end{threeparttable}
\end{table}
```

--------------------------------

### 创建三线表

Source: https://context7.com/deedive/bjfuthesis/llms.txt

使用 booktabs 宏包的 toprule、midrule 和 bottomrule 命令，并配合 bicaption 设置双语标题。

```latex
\begin{table}[htbp]
  \xiaowu  % 设置为小五号字体
  \centering
  \bicaption{不同方法的性能对比}{Performance Comparison of Different Methods}
  \label{tab:performance}
  \begin{tabular}{lccc}
    \toprule
    方法         & 准确率(\%) & 召回率(\%) & F1值(\%)  \\
    \midrule
    基准方法     & 85.2       & 82.1       & 83.6      \\
    改进方法A    & 88.7       & 85.3       & 86.9      \\
    本文方法     & 92.4       & 89.6       & 91.0      \\
    \bottomrule
  \end{tabular}
\end{table}

% 在正文中引用表格
表~\ref{tab:performance} 展示了不同方法在测试集上的性能对比结果。
```

--------------------------------

### Acknowledgements Environment

Source: https://context7.com/deedive/bjfuthesis/llms.txt

Use the acknowledgements environment for writing acknowledgements. It automatically formats the title (size 3 Song font, bold, centered) and the body text (size 5 font). The acknowledgements will be added to the table of contents.

```latex
\phantomsection\addcontentsline{toc}{chapter}{致谢}
\begin{acknowledgements}
This thesis was completed under the careful guidance of Professor XXX. The supervisor's rigorous academic attitude, profound professional knowledge, and spirit of never tiring of teaching have benefited me greatly, and I express my sincere gratitude here.

Thanks to all my classmates in the laboratory for their help and support during the experimental process,
and thanks to my family for their understanding and encouragement over the years.

Finally, thank you to all the reviewers for reviewing this paper in their busy schedules and providing valuable opinions and suggestions.
\end{acknowledgements}
```

--------------------------------

### 编写数学公式

Source: https://context7.com/deedive/bjfuthesis/llms.txt

使用 equation 和 align 环境编写公式，并支持通过 eqref 进行引用。

```latex
% 单行公式
\begin{equation}
  E = mc^2
  \label{eq:einstein}
\end{equation}

% 带分数和求和的复杂公式
\begin{equation}
  L(\theta) = -\frac{1}{N} \sum_{i=1}^{N} \left[ y_i \log(\hat{y}_i) +
              (1-y_i) \log(1-\hat{y}_i) \right]
  \label{eq:cross-entropy}
\end{equation}

% 多行对齐公式
\begin{align}
  \nabla_\theta J(\theta) &= \mathbb{E}_{\pi_\theta}
                             \left[ \nabla_\theta \log \pi_\theta(a|s) A(s,a) \right] \\
  A(s,a) &= Q(s,a) - V(s)
  \label{eq:policy-gradient}
\end{align}

% 在正文中引用公式
如公式~\eqref{eq:cross-entropy} 所示，本文采用交叉熵作为损失函数。
```

--------------------------------

### 编写算法伪代码

Source: https://context7.com/deedive/bjfuthesis/llms.txt

使用 algorithm 和 algorithmic 环境定义算法结构，支持输入输出及逻辑控制。

```latex
\renewcommand{\algorithmicrequire}{\textbf{输入：}\unskip}
\renewcommand{\algorithmicensure}{\textbf{输出：}\unskip}

\begin{algorithm}[htbp]
  \bicaption{梯度下降优化算法}{Gradient Descent Optimization Algorithm}
  \label{alg:gradient-descent}
  \small
  \begin{algorithmic}
    \REQUIRE 初始参数 $\theta_0$，学习率 $\alpha$，最大迭代次数 $T$
    \ENSURE 优化后的参数 $\theta^*$

    \STATE $t \leftarrow 0$
    \WHILE{$t < T$ \AND not converged}
      \STATE 计算梯度 $g_t \leftarrow \nabla_\theta L(\theta_t)$
      \STATE 更新参数 $\theta_{t+1} \leftarrow \theta_t - \alpha \cdot g_t$
      \IF{$\|g_t\| < \epsilon$}
        \STATE \textbf{break}
      \ENDIF
      \STATE $t \leftarrow t + 1$
    \ENDWHILE
    \RETURN $\theta_t$
  \end{algorithmic}
\end{algorithm}

% 在正文中引用算法
算法~\ref{alg:gradient-descent} 描述了基本的梯度下降优化过程。
```

--------------------------------

### Chinese Abstract Environment

Source: https://context7.com/deedive/bjfuthesis/llms.txt

The abstract environment handles Chinese abstract formatting and keyword output.

```latex
\begin{abstract}
  论文的摘要是对论文研究内容和成果的高度概括。
  摘要应具有独立性和自明性，其内容应包含与论文全文同等量的主要信息。
  使读者即使不阅读全文，通过摘要就能了解论文的总体内容和主要成果。

  论文摘要的书写应力求精确、简明。
  切忌写成对论文书写内容进行提要的形式，
  尤其要避免"第1章……；第2章……；……"这种或类似的陈述方式。

  关键词是为了文献标引工作、用以表示全文主要内容信息的单词或术语。
  关键词不超过5个，每个关键词中间用分号分隔。

  \bjfusetup{
    keywords = {机器学习, 深度神经网络, 图像识别, 自然语言处理, 数据挖掘},
  }
\end{abstract}
```

--------------------------------

### Custom Font Size Commands

Source: https://context7.com/deedive/bjfuthesis/llms.txt

The template predefines custom font size commands compliant with Chinese standards, ranging from Chuhao to Bahao (16 sizes). These can be used anywhere to adjust local text size.

```latex
% Available font size commands and their corresponding point values
\chuhao   % Chuhao 42pt
\xiaochu  % Xiao Chu 36pt
\yihao    % Yihao 26pt
\xiaoyi   % Xiao Yi 24pt
\erhao    % Erhao 22pt
\xiaoer   % Xiao Er 18pt
\sanhao   % Sanhao 16pt
\xiaosan  % Xiao San 15pt
\sihao    % Sihao 14pt
\xiaosi   % Xiao Si 12pt
\wuhao    % Wuhao 10.5pt
\xiaowu   % Xiao Wu 9pt
\liuhao   % Liuhao 7.5pt
\xiaoliu  % Xiao Liu 6.5pt
\qihao    % Qihao 5.5pt
\bahao    % Ba hao 5pt

% Usage example
{\sanhao\bfseries This is Sanhao bold text}

{\xiaowu Table content uses Xiao Wu font}
```

--------------------------------

### 创建跨页长表格

Source: https://context7.com/deedive/bjfuthesis/llms.txt

使用 longtable 宏包处理跨页数据，确保表头在每页重复显示。

```latex
{
  \xiaowu
  \centering
  \captionof{table}{实验数据记录 \\ Table \ref{tab:long-data} Experimental Data Records}
  \addtocounter{table}{-1}
  \begin{longtabu}[h]{lccc}
    \label{tab:long-data} \\
    \toprule
    序号 & 样本ID & 测量值 & 标准差 \\
    \midrule
    \endhead
    1    & S001   & 23.45  & 0.12   \\
    2    & S002   & 24.67  & 0.15   \\
    3    & S003   & 22.89  & 0.11   \\
    % ... 更多数据行
    \bottomrule
  \end{longtabu}
}
```

--------------------------------

### VS Code Code Snippets for Tables

Source: https://context7.com/deedive/bjfuthesis/llms.txt

This VS Code snippet generates a basic three-line table using the \tabcolsep and \arrayrulewidth commands for column spacing and rule width. It includes Chinese and English captions and a label for cross-referencing.

```latex
\begin{table}[h]
  \xiaowu
  \centering
  \begin{threeparttable}
    \bicaption{中文标题}{English Title}
    \label{Table:label}
    \begin{tabular}{ccc}
      \toprule
      Column 1 & Column 2 & Column 3 \\
      \midrule
      Data 1 & Data 2 & Data 3 \\
      \bottomrule
    \end{tabular}
  \end{threeparttable}
\end{table}
```

--------------------------------

### Insert Images with Bilingual Captions

Source: https://context7.com/deedive/bjfuthesis/llms.txt

Use the figure environment and bicaption for bilingual figure titles.

```latex
% 插入单张图片
\begin{figure}[htbp]
  \centering
  \includegraphics[width=0.7\textwidth]{static/example.png}
  \caption*{此处添加图注，有图注时需置于图题之上}
  \bicaption{实验结果对比图}{Comparison of Experimental Results}
  \label{fig:result-comparison}
\end{figure}

% 在正文中引用图片
如图~\ref{fig:result-comparison} 所示，本文提出的方法在准确率上优于基准方法。
```

--------------------------------

### Main Document Structure

Source: https://context7.com/deedive/bjfuthesis/llms.txt

The primary structure for a thesis document using the bjfuthesis class, including front matter, main matter, and back matter.

```latex
% bjfuthesis-main.tex - 主文档文件
% !TeX encoding = UTF-8
% !TeX program = xelatex

\documentclass[AutoFakeBold]{bjfuthesis}

% 加载配置文件（包含宏包和基本设置）
\input{bjfusetup}

\begin{document}

% 导入封面（需先填写 Word 模板并转换为 PDF）
\includepdf[pages=1]{static/coverpage.pdf}

% 导入独创性声明
\includepdf[pages=1]{static/statement.pdf}

% 前置部分（摘要、目录等，使用罗马数字页码）
\frontmatter
\input{contents/abstract}
\tableofcontents

% 正文部分（使用阿拉伯数字页码）
\mainmatter
\input{contents/chap00}
\input{contents/chap01}

% 后置部分
\backmatter

% 参考文献
\phantomsection\addcontentsline{toc}{chapter}{参考文献}
\bibliography{bibdata/refs}

% 致谢
\phantomsection\addcontentsline{toc}{chapter}{致谢}
\input{contents/acknowledgements}

% 附录
\appendix
\input{contents/appendix}

\end{document}
```

--------------------------------

### Insert Subfigures

Source: https://context7.com/deedive/bjfuthesis/llms.txt

Use subcaptionbox to group multiple images within a single figure environment.

```latex
\begin{figure}[htbp]
  \centering
  \subcaptionbox{训练损失曲线\label{fig:loss-curve}}
    {\includegraphics[width=0.45\linewidth]{static/loss.png}}
  \hspace{0.05\linewidth}
  \subcaptionbox{验证准确率曲线\label{fig:acc-curve}}
    {\includegraphics[width=0.45\linewidth]{static/accuracy.png}}
  \bicaption{模型训练过程}{Model Training Process}
  \label{fig:training-process}
\end{figure}

% 引用主图和子图
如图~\ref{fig:training-process} 所示，模型在训练过程中收敛稳定。
图~\ref{fig:loss-curve} 显示训练损失持续下降，
图~\ref{fig:acc-curve} 显示验证准确率逐步提升。
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.