可以使用Python的`nltk`库（自然语言工具包）来实现文章内容的口语化分句，同时结合一定的规则来保证句子不过长。以下是示例代码： ```python import nltk from nltk.tokenize import sent_tokenize nltk.download('punkt') def split_sentences(text, max_length=30): sentences = sent_tokenize(text) new_sentences = [] for sentence in sentences: if len(sentence) <= max_length: new_sentences.append(sentence) else: words = sentence.split() current_sentence = "" for word in words: if len(current_sentence) + len(word) + 1 <= max_length: if current_sentence: current_sentence += " " + word else: current_sentence = word else: new_sentences.append(current_sentence) current_sentence = word if current_sentence: new_sentences.append(current_sentence) return new_sentences # 示例文本 text = "这是一段示例文本，它用于展示如何进行口语化分句。并且要保证每个句子不会过长。" result = split_sentences(text) for sentence in result: print(sentence) ``` 在上述代码中，定义了`split_sentences`函数，它接受文章文本和最大句子长度作为参数。使用`nltk`的`sent_tokenize`函数对文本进行初步分句，然后遍历每个句子，如果句子长度小于等于最大长度，则直接添加到结果列表中；否则，将句子按单词拆分，逐步构建新的句子，确保每个新句子的长度不超过最大长度。