LaTex中使用tabularx创建表格时边框无法闭合的问题求助

嘿，我来帮你搞定这个tabularx的边框问题！你遇到的核心问题是固定宽度的p列总宽度和\textwidth不匹配，导致添加闭合边框时出现异常排版。下面给你具体的解决思路和修正后的代码：

问题根源分析 #

你当前的列格式是：

{|p{2.3cm}|p{3.5cm}|p{3.5cm}|p{3.5cm}}

• 末尾缺少|导致表格右边框不闭合；
• 直接添加|后，固定列宽+边框宽度+列间距的总宽度超过了\textwidth，tabularx为了强制适配宽度会出现异常，看起来像多出了空白列。

两种可行解决方案 #

方案1：用tabularx的X列自动适配宽度（推荐） #

tabularx的核心优势就是通过X列自动分配剩余宽度，不用手动计算列宽。把列格式改成下面这样，第一列固定宽度，剩下三列自动平分剩余空间，边框会完美闭合：

{|p{2.3cm}|X|X|X|}

方案2：手动调整p列宽度适配\textwidth #

如果坚持用固定宽度的p列，需要精确计算列宽，让总宽度（列宽+边框宽度+列间距）刚好等于\textwidth。默认article类的\textwidth约为15.9cm，计算后把右侧三列的宽度调整为~3.95cm即可：

{|p{2.3cm}|p{3.95cm}|p{3.95cm}|p{3.95cm}|}

额外优化建议 #

1. 移除多余的\makeatother：你代码里没有用到\@开头的命令，这个命令是多余的，直接删掉即可；
2. 优化表格内的列表缩进：用enumitem包替代paralist，可以更灵活地控制列表的缩进和间距，让表格内容更紧凑：

   \usepackage{enumitem}
   % 在表格内的列表使用：
   \begin{itemize}[leftmargin=*, topsep=0pt, partopsep=0pt, itemsep=0pt]
   

修正后的完整代码 #

\documentclass{article} 
\usepackage{tabularx} 
\usepackage{enumitem} % 替换paralist，更灵活控制列表
\begin{document} 
\begin{table}[!b] 
\centering 
\caption{Comparison between ST-Kriging, Bayesian inference, and ANNs.} 
\label{tab:2} 
\renewcommand{\arraystretch}{1.5} 
\scriptsize 
\begin{tabularx}{\textwidth} {|p{2.3cm}|X|X|X|} 
\hline 
\textbf{} & \textbf{Bayesian Inferences} & \textbf{ST-Kriging} & \textbf{ANNs} \\ 
\hline 
\textbf{Computational Complexity} & NP-hard \cite{ref-satria2020spatial} & $O(N^2)$ & $O(i\times o\times n + n\times o)$ or $O(n \times o \times (i+1))$ for training a single epoch. \cite{ref-taylor1995freeway} \\ 
\hline 
\textbf{Performance Evaluation } & Provides a posterior probability distribution with confidence interval. & Ensure linear unbiased predictors. & Epoch with the lowest sum of squared error.\\ 
\hline 
\textbf{Weaknesses} & Very computationally intensive due to choosing the proper prior distribution. & \begin{itemize}[leftmargin=*, topsep=0pt, partopsep=0pt, itemsep=0pt] 
\item Missing value causes error in unmatched dimensions. 
\item Can not handle large datasets. 
\item Require normal distribution. 
\end{itemize} & Require intensive data training, and this might lead to an overfitting problem. \\ 
\hline 
\textbf{Strengths } & \begin{itemize}[leftmargin=*, topsep=0pt, partopsep=0pt, itemsep=0pt] 
\item Handle large and small data. 
\item Handle missing values. 
\item Prior knowledge about uncertain input is not required. 
\end{itemize} & \begin{itemize}[leftmargin=*, topsep=0pt, partopsep=0pt, itemsep=0pt] 
\item Handle small data. 
\item Computational efficiency. 
\end{itemize} & \begin{itemize}[leftmargin=*, topsep=0pt, partopsep=0pt, itemsep=0pt] 
\item Handle big data and small data. 
\item Accommodate missing values without a separate estimation step [108] 
\item Computational efficiency due to the parallelity feature. 
\item Prior knowledge about uncertain input is not required. 
\end{itemize} \\ 
\hline 
\textbf{Overcoming the Limitation} & Use uninformative prior to reduce the computational time, however, it can affect the prediction accuracy negatively. & Remove observations that include missing values. & \begin{itemize}[leftmargin=*, topsep=0pt, partopsep=0pt, itemsep=0pt] 
\item Decrease the number of layers of the network. 
\item Use iterative methods to stop the training process such as gradient descent. 
\end{itemize} \\ 
\hline 
\end{tabularx} 
\end{table} 
\end{document}

内容的提问来源于stack exchange，提问作者Reta