你可以用 regexp_extract 要获取相关字符串：

import pyspark.sql.functions as F

pattern = '|'.join([rf'{word}' for word in word_list if len(word) >= 6 and len(word) <= 11])

df2 = df.withColumn(
    'match',
    F.regexp_extract(
        'text',
        rf"\b({pattern})\b",
        1
    )
).withColumn(
    'match',
    F.when(F.col('match') != '', F.col('match'))    # replace no match with null
)

df2.show(truncate=False)
+----------------------------------+------------+
|text                              |match       |
+----------------------------------+------------+
|This is line one                  |Null        |
|This is line two                  |Null        |
|bla coroner foo bar               |coroner     |
|This is line three                |Null        |
|foo bar shakespeare               |shakespeare |
|Null                              |Null        |
+----------------------------------+------------+

这个 pattern 有点像 \b(word1|word2|word3)\b ，在哪里 \b 表示单词边界（空格/行首/行尾），以及 | 手段 or .

你可以使用这个列表 word_list 作为数组文字并检查数组与列的交集 text :

from pyspark.sql import functions as F

word_list_arr = F.array(*[F.lit(w) for w in word_list if len(w) >= 6 and len(w) <= 11])

df1 = df.withColumn(
    "match",
    F.array_join(F.array_intersect(F.split("text", " "), word_list_arr), " ")
).withColumn("match", F.expr("nullif(match, '')"))

df1.show(truncate=False)

# +----------------------------------+------------+

# |text                              |match       |

# +----------------------------------+------------+

# |This is line one                  |Null        |

# |This is line two                  |Null        |

# |bla coroner foo bar               |coroner     |

# |This is line three                |Null        |

# |foo bar shakespeare               |shakespeare |

# |Null                              |Null        |

# +----------------------------------+------------+