您可以使用pandas Python库来实现此目的。
为了读取文本文件并将其保存为csv，您可以这样做：

import pandas as pd

def txt_to_csv(pathToTxtFile, pathToCsvFile):
    # reading the txt file
    df = pd.read_csv(<pathToTxtFile>, skiprows=1, sep=' ')

    # renaming columns
    new_col_name = f'{df.columns[1]} {df.columns[2]} {df.columns[3]}'
    df.rename(columns={df.columns[1]: new_col_name}, inplace=True)

    # dropping extra columns
    df = df.drop(columns=[df.columns[2], df.columns[3]])

    # saving as csv
    df.to_csv(<pathToCsvFile>, index=False)

为了对多个文件自动执行此操作，您可以将它们全部存储在一个文件夹中，然后循环它们并在循环中调用上面的函数txt_to_csv，有点像这样：

import os

for txtfile in os.listdir("<pathToFolder>"):
    if txtfile.lower().endswith(".txt"):
        txt_to_csv(txtfile, os.path.splitext(txtfile)[0]+".csv")

此脚本将使用相同的文件名以.csv将它们全部保存在同一文件夹中

如果每个文件的标题不同，你必须调整。而且，我是多线程的新手，这可能是矫枉过正。
https://onlinegdb.com/5S2R-yjpD

import csv
from pathlib import Path
from concurrent.futures import ThreadPoolExecutor

def worker(file, csv_file):
    print(f'converting file: {file} -> {csv_file}')
    with open(file, newline=None) as IN, open(csv_file, 'w', newline=None) as OUT:
        next(IN) # throwaway first line
        headers = next(IN).strip().split(maxsplit=1) # adjust if files vary
        rows = (row for row in csv.reader(IN, delimiter=' '))
        csv.writer(OUT).writerows((headers, *rows))
    print(f'done converting file: {file} -> {csv_file}')

with ThreadPoolExecutor(10) as e:
    # adjust your path accordingly
    for file in Path('.').glob('*.txt'):
        csv_file = f'{file.stem}.csv'
        e.submit(worker, file, csv_file)