我正在尝试使用 LaTeX 创建表格，但效果并不理想。存在一些问题：下一页溢出，文本分散，对齐不良。我还想在列名中添加更多文本idea。
请查看下面给出的代码。
\documentclass{article}
\begin{document}
\begin{center}
\begin{tabular}{|p{0.4cm}||p{2cm}|p{2cm}|p{2cm}| p{2cm}|p{2cm}|p{2cm}|}
\hline
\multicolumn{7}{|c|}{\textbf{Data Structures}} \\
\hline
\textbf{No} & \textbf{Sigma name and all} & \textbf{Idea}& \textbf{Construct Time(Runtime Time)} & \textbf{Construct Time (Space)} & \textbf{Searching data (Query)} & \textbf{Search head (Space)}\\
\hline
1 & ABCD & These algorithms achieve performance better than the classic & O(NLkt) & O(nL)&O(L(kt + dnp)&O(1)\\ \hline
2& FGH & is an algorithm for solving the approximate or exact Near Neighbor Search in high dimensional spaces & $O(dn \log n)$ & $O(dn)$ &222& $O(1)$\\ \hline
3&LMPQ & is an algorithm for solving the approximate or exact Near Neighbor Search in high dimensional spaces & 008 & 666 & 242&333\\ \hline
4 & RSTV & is an algorithm for solving the approximate or exact Near Neighbor Search in high dimensional spaces & 012 & 888 & 333&55\\ \hline
5& ZMNQ & is an algorithm for solving the approximate or exact Near Neighbor Search in high dimensional spaces &016 & 888 & 444&343\\ \hline
6& WORP & is an algorithm for solving the approximate or exact Near Neighbor Search in high dimensional spaces and MBR &020 &555 & 444&333\\ \hline
7& MNPO & is an algorithm for solving the approximate or exact Near Neighbor Search in high dimensional spaces. Search based on data pruning and the radius of nodes.&024 & 888 &224&342\\ \hline
\hline
\end{tabular}
\end{center}
\end{document}
答案1
一个可能的解决方案：
- 更广泛
\textwidth地取决于使用geometry包 footnotesize字体大小- 不同宽度的列
tabularray包的使用
\documentclass{article}
\usepackage{geometry}
\usepackage{microtype}
\usepackage{tabularray}
\begin{document}
\begin{center}
\footnotesize
\begin{tblr}{hlines, vlines,
colspec = {c X[0.5, c] X[2.2, j] *{4}{X[0.7, c]}},
colsep = 3pt,
row{1,2} = {font=\bfseries, c, m}
}
\SetCell[c=7]{c} Data Structures
& & & & & & \\
No & Sigma name and all
& Idea & Construct Time (Runtime Time)
& Construct Time (Space)
& Searching data (Query)
& Search head (Space) \\
1 & ABCD & These algorithms achieve performance better than the classic
& O(NLkt)
& O(nL) & O(L(kt + dnp)
& O(1) \\
2 & FGH & is an algorithm for solving the approximate or exact Near Neighbor Search in high dimensional spaces
& $O(dn \log n)$
& $O(dn)$
& 222 & $O(1)$ \\
3 & LMPQ & is an algorithm for solving the approximate or exact Near Neighbor Search in high dimensional spaces
& 008 & 666 & 242 & 333 \\
4 & RSTV & is an algorithm for solving the approximate or exact Near Neighbor Search in high dimensional spaces
& 012 & 888 & 333 & 55 \\
5 & ZMNQ & is an algorithm for solving the approximate or exact Near Neighbor Search in high dimensional spaces
& 016 & 888 & 444 & 343 \\
6 & WORP & is an algorithm for solving the approximate or exact Near Neighbor Search in high dimensional spaces and MBR
& 020 & 555 & 444 & 333 \\
7 & MNPO & is an algorithm for solving the approximate or exact Near Neighbor Search in high dimensional spaces. Search based on data pruning and the radius of nodes.
& 024 & 888 & 224 & 342 \\
\end{tblr}
\end{center}
\end{document}
（灰线为页面边框）
附录： 是的，这个表可以用经典方式设置，也可以在 Overleaf 上编译。代码和结果都很优雅，但解决方案有效：
\documentclass{article}
\usepackage{geometry}
\usepackage{microtype}
\usepackage{ragged2e}
\usepackage{tabularx}
\newcolumntype{C}[1]{>{\hsize=#1\hsize\linewidth=\hsize%
\Centering}X}
\newcolumntype{L}[1]{>{\hsize=#1\hsize\linewidth=\hsize%
\RaggedRight\hspace{0pt}}X}
\begin{document}
\begin{center}
\footnotesize
\setlength\tabcolsep{3pt}
\begin{tabularx}{\textwidth}{|c | C{0.7} | L{2.5} | *{4}{C{0.7}|} }
%
\hline
\multicolumn{7}{|c|}{\textbf{Data Structures}} \\
\hline
\textbf{No}
& \textbf{Sigma name and all}
& \centering\textbf{Idea}
& \textbf{Construct Time (Runtime Time)}
& \textbf{Construct Time (Space)}
& \textbf{Searching data (Query)}
& \textbf{Search head (Space)}
\\
\hline
1 & ABCD & These algorithms achieve performance better than the classic
& O(NLkt)
& O(nL) & O(L(kt + dnp)
& O(1) \\
\hline
2 & FGH & is an algorithm for solving the approximate or exact Near Neighbor Search in high dimensional spaces
& $O(dn \log n)$
& $O(dn)$
& 222 & $O(1)$ \\
\hline
3 & LMPQ & is an algorithm for solving the approximate or exact Near Neighbor Search in high dimensional spaces
& 008 & 666 & 242 & 333 \\
\hline
4 & RSTV & is an algorithm for solving the approximate or exact Near Neighbor Search in high dimensional spaces
& 012 & 888 & 333 & 55 \\
\hline
5 & ZMNQ & is an algorithm for solving the approximate or exact Near Neighbor Search in high dimensional spaces
& 016 & 888 & 444 & 343 \\
\hline
6 & WORP & is an algorithm for solving the approximate or exact Near Neighbor Search in high dimensional spaces and MBR
& 020 & 555 & 444 & 333 \\
\hline
7 & MNPO & is an algorithm for solving the approximate or exact Near Neighbor Search in high dimensional spaces. Search based on data pruning and the radius of nodes.
& 024 & 888 & 224 & 342 \\
\hline
\end{tabularx}
\end{center}
\end{document}
