你的代码看起来很好，有一个小的变化
在elif末尾放置和不在stop_words中的str（word）

import spacy    
def preprocess_text_spacy(text):
    stop_words = ["a", "the", "is", "are"]
    nlp = spacy.load('en_core_web_sm')
    tokens = set()
    doc = nlp(text)
    print(doc)
    for word in doc:
        if word.is_currency:
            tokens.add(word.lower_)
        elif len(word.lower_) == 1:
            if word.is_digit and float(word.text) == 0:
                tokens.add(word.text)
        elif not word.is_punct and not word.is_space and not word.is_quote and not word.is_bracket and str(word) not in stop_words:
            tokens.add(word.lower_)
    return list(tokens)

您的条件写错了。您的最后一个elif等价于：

condC = not in stop_words
elif condA and condB and not in condC:
    ...

如果你试图执行这段代码，你会得到一个语法错误，要检查某个元素是否在某个可迭代对象中，你需要在关键字in的左边提供那个元素，你只需要写word：

elif condA and condB and ... and str(word) not in stop_words:
   ...

您需要将stop_words添加到函数中，该函数将停止词列表作为输入，然后您需要修改向标记列表添加单词的条件，以检查单词是否在stop_words列表中

def preprocess_text_spacy(text, stop_words):
    nlp = spacy.load('en_core_web_sm')
    tokens = []
    doc = nlp(text)
    for word in doc:
        if word.is_currency:
            tokens.append(word.lower_)
        elif len(word.lower_) == 1:
            if word.is_digit and float(word.text) == 0:
                tokens.append(word.text)
        elif not word.is_punct and not word.is_space and not word.is_quote and not word.is_bracket and word.lower_ not in stop_words:
            tokens.append(word.lower_)
    return tokens

样品：

text = "This is a sample text to demonstrate the function."
stop_words = ["a", "the", "is", "are"]
tokens = preprocess_text_spacy(text, stop_words)
print(tokens)

输出：

['this', 'sample', 'text', 'to', 'demonstrate', 'function']