Python实现自动生成高质量文章的示例代码

import random
import json

# 加载文章素材库
def load_materials(filename):
    with open(filename, 'r', encoding='utf-8') as f:
        return json.load(f)

# 生成文章段落
def generate_paragraph(materials, min_sentences=3, max_sentences=6):
    sentences = []
    num_sentences = random.randint(min_sentences, max_sentences)
    
    for _ in range(num_sentences):
        # 随机选择句子模板
        template = random.choice(materials['sentence_templates'])
        
        # 填充模板中的占位符
        if '{famous}' in template:
            famous = random.choice(materials['famous_quotes'])
            before = random.choice(materials['before_quote'])
            after = random.choice(materials['after_quote'])
            famous_sentence = f"{before}{famous}{after}"
            sentence = template.replace('{famous}', famous_sentence)
        else:
            sentence = template
        
        # 替换其他占位符
        sentence = sentence.replace('{topic}', random.choice(materials['topics']))
        sentence = sentence.replace('{industry}', random.choice(materials['industries']))
        
        sentences.append(sentence)
    
    return ' '.join(sentences)

# 生成完整文章
def generate_article(materials, min_paragraphs=5, max_paragraphs=8):
    paragraphs = []
    num_paragraphs = random.randint(min_paragraphs, max_paragraphs)
    
    # 生成引言
    intro = generate_paragraph(materials, 2, 3)
    paragraphs.append(f"<h2>引言</h2><p>{intro}</p>")
    
    # 生成正文
    for i in range(1, num_paragraphs - 1):
        paragraph = generate_paragraph(materials)
        paragraphs.append(f"<h3>第{i}部分</h3><p>{paragraph}</p>")
    
    # 生成结论
    conclusion = generate_paragraph(materials, 2, 3)
    paragraphs.append(f"<h2>结论</h2><p>{conclusion}</p>")
    
    return '\n'.join(paragraphs)

# 主程序
if __name__ == "__main__":
    materials = load_materials('article_materials.json')
    article = generate_article(materials)
    print(article)
    with open('generated_article.html', 'w', encoding='utf-8') as f:
        f.write(article)

{
    "famous_quotes": [
        "爱因斯坦曾说过：想象力比知识更重要",
        "乔布斯有句名言：保持饥饿，保持愚蠢",
        "马云认为：今天很残酷，明天更残酷，后天很美好"
    ],
    "before_quote": [
        "正如",
        "记得",
        "让我们想起"
    ],
    "after_quote": [
        "这句话至今仍然发人深省",
        "这正是我们需要思考的",
        "这对我们的启发很大"
    ],
    "sentence_templates": [
        "在{industry}领域，{topic}一直是人们关注的焦点。",
        "近年来，{topic}在{industry}中扮演着越来越重要的角色。{famous}",
        "关于{topic}，业界存在多种不同观点。"
    ],
    "topics": [
        "数字化转型", "人工智能", "内容营销", "用户体验", "数据分析"
    ],
    "industries": [
        "互联网", "金融科技", "教育培训", "医疗健康", "新媒体"
    ]
}

def check_keyword_density(text, keyword):
    word_count = len(text.split())
    keyword_count = text.lower().count(keyword.lower())
    return keyword_count / word_count

import textstat

def assess_readability(text):
    return textstat.flesch_reading_ease(text)

styles = {
    "formal": {
        "sentence_starters": ["研究表明", "数据证明", "专家指出"],
        "transition_words": ["因此", "综上所述", "由此可见"]
    },
    "casual": {
        "sentence_starters": ["你知道吗", "其实", "说实话"],
        "transition_words": ["不过", "话说回来", "你懂的"]
    }
}

from textblob import TextBlob

def adjust_sentiment(text, target_polarity):
    analysis = TextBlob(text)
    current_polarity = analysis.sentiment.polarity
    # 调整逻辑...

def ensure_coherence(paragraphs):
    # 检查段落间逻辑关系
    # 必要时添加过渡句

technical_terms = ["机器学习", "神经网络", "深度学习"]

​​​​​​​def validate_terms(text):
    for term in technical_terms:
        if term in text:
            return True
    return False