作者：七北
*更新时间2025
前言
作为一名拥年全栈开发经验的技术博客，我深知财经类文章原创性对SEO和内容质量的重要性。证监会数据作为权威的金融信息来源，其引用方式和原创性判断一直是财经内容创作者关注的焦点。今天我将从技术角度深入分析财经类文章引用证监会数据的原创性判断标准，以及如何优化内容创作来提升SEO表现
一、财经类文章原创性分
1.1 原创性判断标
*财经类文章原创性分析系
# 财经类文章原创性分析系
class FinancialArticleOriginalityAnalyzer:
def __init__(self):
self.originality_criteria = {
'content_originality': '内容原创,
'data_interpretation': '数据解读',
'analysis_depth': '分析深度',
'insight_quality': '洞察质量',
'writing_style': '写作风格',
'value_addition': '价值增
}
self.originality_factors = {
'data_source_attribution': '数据来源归属',
'analysis_originality': '分析原创,
'insight_originality': '洞察原创,
'writing_originality': '写作原创,
'value_originality': '价值原创
}
def analyze_financial_article_originality(self, article_data, csrc_data):
"""
分析财经类文章原创
"""
originality_analysis = {
'data_usage_analysis': {},
'content_originality_analysis': {},
'value_addition_analysis': {},
'originality_score': 0.0,
'improvement_suggestions': [],
'seo_optimization_opportunities': []
}
# 数据使用分析
data_usage_analysis = self.analyze_data_usage(article_data, csrc_data)
originality_analysis['data_usage_analysis'] = data_usage_analysis
# 内容原创性分
content_originality = self.analyze_content_originality(article_data)
originality_analysis['content_originality_analysis'] = content_originality
# 价值增值分
value_addition = self.analyze_value_addition(article_data, csrc_data)
originality_analysis['value_addition_analysis'] = value_addition
# 计算原创性分
originality_score = self.calculate_originality_score(
data_usage_analysis, content_originality, value_addition
)
originality_analysis['originality_score'] = originality_score
# 改进建议
improvement_suggestions = self.generate_improvement_suggestions(originality_analysis)
originality_analysis['improvement_suggestions'] = improvement_suggestions
# SEO优化机会
seo_opportunities = self.identify_seo_optimization_opportunities(originality_analysis)
originality_analysis['seo_optimization_opportunities'] = seo_opportunities
return originality_analysis
def analyze_data_usage(self, article_data, csrc_data):
"""
分析数据使用情况
"""
data_usage_analysis = {
'data_sources': [],
'data_attribution': {},
'data_interpretation': {},
'data_originality': {},
'data_value_addition': {}
}
# 识别数据来源
data_sources = self.identify_data_sources(article_data)
data_usage_analysis['data_sources'] = data_sources
# 分析数据归属
data_attribution = self.analyze_data_attribution(article_data, csrc_data)
data_usage_analysis['data_attribution'] = data_attribution
# 分析数据解读
data_interpretation = self.analyze_data_interpretation(article_data, csrc_data)
data_usage_analysis['data_interpretation'] = data_interpretation
# 分析数据原创
data_originality = self.analyze_data_originality(article_data, csrc_data)
data_usage_analysis['data_originality'] = data_originality
# 分析数据价值增
data_value_addition = self.analyze_data_value_addition(article_data, csrc_data)
data_usage_analysis['data_value_addition'] = data_value_addition
return data_usage_analysis
def identify_data_sources(self, article_data):
"""
识别数据来源
"""
data_sources = {
'csrc_data': [],
'other_official_data': [],
'third_party_data': [],
'internal_data': [],
'estimated_data': []
}
# 识别证监会数
csrc_indicators = [
'证监, 'CSRC', '中国证监, '证券监督管理委员,
'上市公司', 'IPO', '再融, '并购重组', '信息披露'
]
for indicator in csrc_indicators:
if indicator in article_data.get('content', ''):
data_sources['csrc_data'].append({
'indicator': indicator,
'context': self.extract_context(article_data['content'], indicator),
'data_type': self.classify_csrc_data_type(indicator)
})
# 识别其他官方数据
official_indicators = [
'央行', '银保监会', '财政, '统计局', '发改,
'人民银行', '银保, '国家统计局'
]
for indicator in official_indicators:
if indicator in article_data.get('content', ''):
data_sources['other_official_data'].append({
'indicator': indicator,
'context': self.extract_context(article_data['content'], indicator),
'data_type': self.classify_official_data_type(indicator)
})
return data_sources
def analyze_data_attribution(self, article_data, csrc_data):
"""
分析数据归属
"""
data_attribution = {
'attribution_quality': 0.0,
'attribution_completeness': 0.0,
'attribution_accuracy': 0.0,
'attribution_standards': [],
'improvement_areas': []
}
# 分析归属质量
attribution_quality = self.evaluate_attribution_quality(article_data)
data_attribution['attribution_quality'] = attribution_quality
# 分析归属完整
attribution_completeness = self.evaluate_attribution_completeness(article_data)
data_attribution['attribution_completeness'] = attribution_completeness
# 分析归属准确
attribution_accuracy = self.evaluate_attribution_accuracy(article_data, csrc_data)
data_attribution['attribution_accuracy'] = attribution_accuracy
# 归属标准
attribution_standards = [
'明确标注数据来源',
'提供数据发布时间',
'说明数据获取方式',
'注明数据使用范围',
'保持数据完整
]
data_attribution['attribution_standards'] = attribution_standards
# 改进领域
improvement_areas = self.identify_attribution_improvement_areas(data_attribution)
data_attribution['improvement_areas'] = improvement_areas
return data_attribution
def analyze_data_interpretation(self, article_data, csrc_data):
"""
分析数据解读
"""
data_interpretation = {
'interpretation_depth': 0.0,
'interpretation_originality': 0.0,
'interpretation_accuracy': 0.0,
'interpretation_insights': [],
'interpretation_methodology': {}
}
# 分析解读深度
interpretation_depth = self.evaluate_interpretation_depth(article_data)
data_interpretation['interpretation_depth'] = interpretation_depth
# 分析解读原创
interpretation_originality = self.evaluate_interpretation_originality(article_data)
data_interpretation['interpretation_originality'] = interpretation_originality
# 分析解读准确
interpretation_accuracy = self.evaluate_interpretation_accuracy(article_data, csrc_data)
data_interpretation['interpretation_accuracy'] = interpretation_accuracy
# 解读洞察
interpretation_insights = self.extract_interpretation_insights(article_data)
data_interpretation['interpretation_insights'] = interpretation_insights
# 解读方法
interpretation_methodology = self.analyze_interpretation_methodology(article_data)
data_interpretation['interpretation_methodology'] = interpretation_methodology
return data_interpretation
def analyze_content_originality(self, article_data):
"""
分析内容原创
"""
content_originality = {
'writing_originality': 0.0,
'analysis_originality': 0.0,
'insight_originality': 0.0,
'structure_originality': 0.0,
'style_originality': 0.0,
'overall_originality': 0.0
}
# 写作原创
writing_originality = self.evaluate_writing_originality(article_data)
content_originality['writing_originality'] = writing_originality
# 分析原创
analysis_originality = self.evaluate_analysis_originality(article_data)
content_originality['analysis_originality'] = analysis_originality
# 洞察原创
insight_originality = self.evaluate_insight_originality(article_data)
content_originality['insight_originality'] = insight_originality
# 结构原创
structure_originality = self.evaluate_structure_originality(article_data)
content_originality['structure_originality'] = structure_originality
# 风格原创
style_originality = self.evaluate_style_originality(article_data)
content_originality['style_originality'] = style_originality
# 总体原创
overall_originality = self.calculate_overall_originality(content_originality)
content_originality['overall_originality'] = overall_originality
return content_originality
def evaluate_writing_originality(self, article_data):
"""
评估写作原创
"""
writing_originality_factors = {
'sentence_structure': 0.0,
'vocabulary_usage': 0.0,
'paragraph_organization': 0.0,
'transition_quality': 0.0,
'tone_consistency': 0.0
}
content = article_data.get('content', '')
# 句子结构分析
sentences = content.split(')
sentence_variety = self.analyze_sentence_variety(sentences)
writing_originality_factors['sentence_structure'] = sentence_variety
# 词汇使用分析
vocabulary_diversity = self.analyze_vocabulary_diversity(content)
writing_originality_factors['vocabulary_usage'] = vocabulary_diversity
# 段落组织分析
paragraph_organization = self.analyze_paragraph_organization(content)
writing_originality_factors['paragraph_organization'] = paragraph_organization
# 过渡质量分析
transition_quality = self.analyze_transition_quality(content)
writing_originality_factors['transition_quality'] = transition_quality
# 语调一致性分
tone_consistency = self.analyze_tone_consistency(content)
writing_originality_factors['tone_consistency'] = tone_consistency
# 计算写作原创性分
writing_originality = sum(writing_originality_factors.values()) / len(writing_originality_factors)
return writing_originality
def analyze_sentence_variety(self, sentences):
"""
分析句子多样
"""
if not sentences:
return 0.0
sentence_lengths = [len(sentence) for sentence in sentences if sentence.strip()]
if not sentence_lengths:
return 0.0
# 计算句子长度标准
mean_length = sum(sentence_lengths) / len(sentence_lengths)
variance = sum((length - mean_length) ** 2 for length in sentence_lengths) / len(sentence_lengths)
std_deviation = variance ** 0.5
# 标准化分数（0-1
variety_score = min(std_deviation / mean_length, 1.0) if mean_length > 0 else 0.0
return variety_score
def analyze_vocabulary_diversity(self, content):
"""
分析词汇多样
"""
if not content:
return 0.0
# 分词（简单实现）
words = content.split()
if not words:
return 0.0
# 计算词汇多样性（Type-Token Ratio
unique_words = set(words)
total_words = len(words)
diversity_ratio = len(unique_words) / total_words if total_words > 0 else 0.0
return diversity_ratio
def analyze_paragraph_organization(self, content):
"""
分析段落组织
"""
if not content:
return 0.0
paragraphs = content.split('nn')
if len(paragraphs) < 2:
return 0.0
# 分析段落长度分布
paragraph_lengths = [len(paragraph) for paragraph in paragraphs if paragraph.strip()]
if not paragraph_lengths:
return 0.0
# 计算段落长度标准
mean_length = sum(paragraph_lengths) / len(paragraph_lengths)
variance = sum((length - mean_length) ** 2 for length in paragraph_lengths) / len(paragraph_lengths)
std_deviation = variance ** 0.5
# 标准化分
organization_score = min(std_deviation / mean_length, 1.0) if mean_length > 0 else 0.0
return organization_score1.2 证监会数据引用规
*证监会数据引用规范系
# 证监会数据引用规范系
class CSRCDataCitationStandards:
def __init__(self):
self.citation_standards = {
'data_source_attribution': '数据来源归属',
'data_accuracy_verification': '数据准确性验,
'data_usage_scope': '数据使用范围',
'data_update_frequency': '数据更新频率',
'data_interpretation_guidelines': '数据解读指南'
}
self.citation_requirements = {
'mandatory_attribution': '强制归属',
'data_verification': '数据验证',
'usage_limitations': '使用限制',
'update_requirements': '更新要求',
'interpretation_standards': '解读标准'
}
def analyze_csrc_data_citation_compliance(self, article_data, csrc_data):
"""
分析证监会数据引用合规
"""
citation_compliance = {
'attribution_compliance': {},
'accuracy_compliance': {},
'usage_compliance': {},
'update_compliance': {},
'interpretation_compliance': {},
'overall_compliance_score': 0.0
}
# 归属合规
attribution_compliance = self.analyze_attribution_compliance(article_data, csrc_data)
citation_compliance['attribution_compliance'] = attribution_compliance
# 准确性合规
accuracy_compliance = self.analyze_accuracy_compliance(article_data, csrc_data)
citation_compliance['accuracy_compliance'] = accuracy_compliance
# 使用合规
usage_compliance = self.analyze_usage_compliance(article_data, csrc_data)
citation_compliance['usage_compliance'] = usage_compliance
# 更新合规
update_compliance = self.analyze_update_compliance(article_data, csrc_data)
citation_compliance['update_compliance'] = update_compliance
# 解读合规
interpretation_compliance = self.analyze_interpretation_compliance(article_data, csrc_data)
citation_compliance['interpretation_compliance'] = interpretation_compliance
# 计算总体合规分数
overall_score = self.calculate_overall_compliance_score(citation_compliance)
citation_compliance['overall_compliance_score'] = overall_score
return citation_compliance
def analyze_attribution_compliance(self, article_data, csrc_data):
"""
分析归属合规
"""
attribution_compliance = {
'source_attribution': False,
'data_attribution': False,
'time_attribution': False,
'method_attribution': False,
'compliance_score': 0.0
}
content = article_data.get('content', '')
# 检查数据来源归
source_indicators = ['证监, 'CSRC', '中国证监]
attribution_compliance['source_attribution'] = any(
indicator in content for indicator in source_indicators
)
# 检查数据归
data_indicators = ['数据来源', '信息来源', '引用来源']
attribution_compliance['data_attribution'] = any(
indicator in content for indicator in data_indicators
)
# 检查时间归
time_indicators = ['发布时间', '数据时间', '统计时间', '截至']
attribution_compliance['time_attribution'] = any(
indicator in content for indicator in time_indicators
)
# 检查方法归
method_indicators = ['统计方法', '计算方法', '数据来源', '获取方式']
attribution_compliance['method_attribution'] = any(
indicator in content for indicator in method_indicators
)
# 计算合规分数
compliance_factors = [
attribution_compliance['source_attribution'],
attribution_compliance['data_attribution'],
attribution_compliance['time_attribution'],
attribution_compliance['method_attribution']
]
attribution_compliance['compliance_score'] = sum(compliance_factors) / len(compliance_factors)
return attribution_compliance
def analyze_accuracy_compliance(self, article_data, csrc_data):
"""
分析准确性合规
"""
accuracy_compliance = {
'data_verification': False,
'source_verification': False,
'calculation_verification': False,
'interpretation_verification': False,
'compliance_score': 0.0
}
content = article_data.get('content', '')
# 检查数据验
verification_indicators = ['验证', '核实', '确认', '核对']
accuracy_compliance['data_verification'] = any(
indicator in content for indicator in verification_indicators
)
# 检查来源验
source_verification_indicators = ['官方数据', '权威数据', '公开数据']
accuracy_compliance['source_verification'] = any(
indicator in content for indicator in source_verification_indicators
)
# 检查计算验
calculation_indicators = ['计算', '统计', '分析', '测算']
accuracy_compliance['calculation_verification'] = any(
indicator in content for indicator in calculation_indicators
)
# 检查解读验
interpretation_indicators = ['解读', '分析', '说明', '解释']
accuracy_compliance['interpretation_verification'] = any(
indicator in content for indicator in interpretation_indicators
)
# 计算合规分数
compliance_factors = [
accuracy_compliance['data_verification'],
accuracy_compliance['source_verification'],
accuracy_compliance['calculation_verification'],
accuracy_compliance['interpretation_verification']
]
accuracy_compliance['compliance_score'] = sum(compliance_factors) / len(compliance_factors)
return accuracy_compliance二、财经类文章SEO优化策略
2.1 内容原创性优
*财经类文章内容原创性优化系
# 财经类文章内容原创性优化系
class FinancialArticleContentOptimizer:
def __init__(self):
self.optimization_areas = {
'data_interpretation': '数据解读',
'analysis_depth': '分析深度',
'insight_quality': '洞察质量',
'writing_originality': '写作原创,
'value_addition': '价值增,
'seo_optimization': 'SEO优化'
}
def optimize_financial_article_content(self, article_data, csrc_data):
"""
优化财经类文章内
"""
content_optimization = {
'data_interpretation_optimization': {},
'analysis_depth_optimization': {},
'insight_quality_optimization': {},
'writing_originality_optimization': {},
'value_addition_optimization': {},
'seo_optimization': {}
}
# 数据解读优化
data_interpretation = self.optimize_data_interpretation(article_data, csrc_data)
content_optimization['data_interpretation_optimization'] = data_interpretation
# 分析深度优化
analysis_depth = self.optimize_analysis_depth(article_data)
content_optimization['analysis_depth_optimization'] = analysis_depth
# 洞察质量优化
insight_quality = self.optimize_insight_quality(article_data)
content_optimization['insight_quality_optimization'] = insight_quality
# 写作原创性优
writing_originality = self.optimize_writing_originality(article_data)
content_optimization['writing_originality_optimization'] = writing_originality
# 价值增值优
value_addition = self.optimize_value_addition(article_data, csrc_data)
content_optimization['value_addition_optimization'] = value_addition
# SEO优化
seo_optimization = self.optimize_seo(article_data)
content_optimization['seo_optimization'] = seo_optimization
return content_optimization
def optimize_data_interpretation(self, article_data, csrc_data):
"""
优化数据解读
"""
data_interpretation_optimization = {
'current_interpretation_analysis': {},
'optimization_strategies': [],
'enhanced_interpretation': {},
'implementation_plan': {}
}
# 当前解读分析
current_analysis = self.analyze_current_data_interpretation(article_data, csrc_data)
data_interpretation_optimization['current_interpretation_analysis'] = current_analysis
# 优化策略
optimization_strategies = [
'增加数据背景说明',
'提供多维度数据解,
'添加数据趋势分析',
'结合行业对比分析',
'提供数据预测和展
]
data_interpretation_optimization['optimization_strategies'] = optimization_strategies
# 增强解读
enhanced_interpretation = self.create_enhanced_data_interpretation(article_data, csrc_data)
data_interpretation_optimization['enhanced_interpretation'] = enhanced_interpretation
return data_interpretation_optimization
def create_enhanced_data_interpretation(self, article_data, csrc_data):
"""
创建增强的数据解
"""
enhanced_interpretation = {
'data_background': {},
'multi_dimensional_analysis': {},
'trend_analysis': {},
'industry_comparison': {},
'prediction_analysis': {}
}
# 数据背景
data_background = {
'data_source_description': '详细描述数据来源和背,
'data_collection_method': '说明数据收集方法',
'data_quality_assessment': '评估数据质量',
'data_limitations': '说明数据局限
}
enhanced_interpretation['data_background'] = data_background
# 多维度分
multi_dimensional_analysis = {
'quantitative_analysis': '定量分析',
'qualitative_analysis': '定性分,
'comparative_analysis': '对比分析',
'correlation_analysis': '相关性分
}
enhanced_interpretation['multi_dimensional_analysis'] = multi_dimensional_analysis
# 趋势分析
trend_analysis = {
'historical_trend': '历史趋势分析',
'current_trend': '当前趋势分析',
'future_trend': '未来趋势预测',
'trend_drivers': '趋势驱动因素'
}
enhanced_interpretation['trend_analysis'] = trend_analysis
return enhanced_interpretation2.2 SEO优化策略
财经类文章SEO优化系统
# 财经类文章SEO优化系统
class FinancialArticleSEOOptimizer:
def __init__(self):
self.seo_optimization_areas = {
'keyword_optimization': '关键词优,
'title_optimization': '标题优化',
'meta_optimization': '元数据优,
'content_structure': '内容结构',
'internal_linking': '内部链接',
'external_linking': '外部链接'
}
def optimize_financial_article_seo(self, article_data):
"""
优化财经类文章SEO
"""
seo_optimization = {
'keyword_optimization': {},
'title_optimization': {},
'meta_optimization': {},
'content_structure_optimization': {},
'internal_linking_optimization': {},
'external_linking_optimization': {}
}
# 关键词优
keyword_optimization = self.optimize_keywords(article_data)
seo_optimization['keyword_optimization'] = keyword_optimization
# 标题优化
title_optimization = self.optimize_title(article_data)
seo_optimization['title_optimization'] = title_optimization
# 元数据优
meta_optimization = self.optimize_meta_data(article_data)
seo_optimization['meta_optimization'] = meta_optimization
# 内容结构优化
content_structure = self.optimize_content_structure(article_data)
seo_optimization['content_structure_optimization'] = content_structure
# 内部链接优化
internal_linking = self.optimize_internal_linking(article_data)
seo_optimization['internal_linking_optimization'] = internal_linking
# 外部链接优化
external_linking = self.optimize_external_linking(article_data)
seo_optimization['external_linking_optimization'] = external_linking
return seo_optimization
def optimize_keywords(self, article_data):
"""
优化关键
"""
keyword_optimization = {
'primary_keywords': [],
'secondary_keywords': [],
'long_tail_keywords': [],
'keyword_density': {},
'keyword_placement': {},
'keyword_optimization_score': 0.0
}
content = article_data.get('content', '')
# 识别主要关键
primary_keywords = self.identify_primary_keywords(content)
keyword_optimization['primary_keywords'] = primary_keywords
# 识别次要关键
secondary_keywords = self.identify_secondary_keywords(content)
keyword_optimization['secondary_keywords'] = secondary_keywords
# 识别长尾关键
long_tail_keywords = self.identify_long_tail_keywords(content)
keyword_optimization['long_tail_keywords'] = long_tail_keywords
# 关键词密度分
keyword_density = self.analyze_keyword_density(content, primary_keywords)
keyword_optimization['keyword_density'] = keyword_density
# 关键词位置分
keyword_placement = self.analyze_keyword_placement(content, primary_keywords)
keyword_optimization['keyword_placement'] = keyword_placement
# 计算关键词优化分
optimization_score = self.calculate_keyword_optimization_score(keyword_optimization)
keyword_optimization['keyword_optimization_score'] = optimization_score
return keyword_optimization
def identify_primary_keywords(self, content):
"""
识别主要关键
"""
financial_keywords = [
'证监, '上市公司', 'IPO', '再融, '并购重组',
'信息披露', '财务数据', '业绩报告', '监管政策',
'资本市场', '证券投资', '风险管理', '合规管理'
]
primary_keywords = []
for keyword in financial_keywords:
if keyword in content:
frequency = content.count(keyword)
primary_keywords.append({
'keyword': keyword,
'frequency': frequency,
'relevance_score': self.calculate_keyword_relevance(keyword, content)
})
# 按相关性排
primary_keywords.sort(key=lambda x: x['relevance_score'], reverse=True)
return primary_keywords[:5] # 返回个主要关键词
def calculate_keyword_relevance(self, keyword, content):
"""
计算关键词相关
"""
# 简单的相关性计
frequency = content.count(keyword)
content_length = len(content)
# 频率分数
frequency_score = frequency / content_length if content_length > 0 else 0
# 位置分数（标题、开头、结尾权重更高）
position_score = 0
if keyword in content[:100]: # 开00字符
position_score += 0.3
if keyword in content[-100:]: # 结尾100字符
position_score += 0.2
# 综合相关性分
relevance_score = frequency_score * 0.7 + position_score * 0.3
return relevance_score三、财经类文章质量评估
3.1 内容质量评估
*财经类文章内容质量评估系
# 财经类文章内容质量评估系
class FinancialArticleQualityAssessor:
def __init__(self):
self.quality_dimensions = {
'content_quality': '内容质量',
'data_quality': '数据质量',
'analysis_quality': '分析质量',
'writing_quality': '写作质量',
'seo_quality': 'SEO质量',
'user_experience_quality': '用户体验质量'
}
def assess_financial_article_quality(self, article_data):
"""
评估财经类文章质
"""
quality_assessment = {
'content_quality_score': 0.0,
'data_quality_score': 0.0,
'analysis_quality_score': 0.0,
'writing_quality_score': 0.0,
'seo_quality_score': 0.0,
'user_experience_quality_score': 0.0,
'overall_quality_score': 0.0,
'quality_improvement_suggestions': []
}
# 内容质量评估
content_quality = self.assess_content_quality(article_data)
quality_assessment['content_quality_score'] = content_quality['score']
# 数据质量评估
data_quality = self.assess_data_quality(article_data)
quality_assessment['data_quality_score'] = data_quality['score']
# 分析质量评估
analysis_quality = self.assess_analysis_quality(article_data)
quality_assessment['analysis_quality_score'] = analysis_quality['score']
# 写作质量评估
writing_quality = self.assess_writing_quality(article_data)
quality_assessment['writing_quality_score'] = writing_quality['score']
# SEO质量评估
seo_quality = self.assess_seo_quality(article_data)
quality_assessment['seo_quality_score'] = seo_quality['score']
# 用户体验质量评估
ux_quality = self.assess_user_experience_quality(article_data)
quality_assessment['user_experience_quality_score'] = ux_quality['score']
# 计算总体质量分数
quality_scores = [
quality_assessment['content_quality_score'],
quality_assessment['data_quality_score'],
quality_assessment['analysis_quality_score'],
quality_assessment['writing_quality_score'],
quality_assessment['seo_quality_score'],
quality_assessment['user_experience_quality_score']
]
quality_assessment['overall_quality_score'] = sum(quality_scores) / len(quality_scores)
# 生成质量改进建议
improvement_suggestions = self.generate_quality_improvement_suggestions(quality_assessment)
quality_assessment['quality_improvement_suggestions'] = improvement_suggestions
return quality_assessment
def assess_content_quality(self, article_data):
"""
评估内容质量
"""
content_quality = {
'score': 0.0,
'factors': {},
'strengths': [],
'weaknesses': []
}
content = article_data.get('content', '')
# 内容长度评估
content_length = len(content)
length_score = min(content_length / 2000, 1.0) # 2000字符为满
# 内容深度评估
depth_score = self.assess_content_depth(content)
# 内容原创性评
originality_score = self.assess_content_originality(content)
# 内容准确性评
accuracy_score = self.assess_content_accuracy(content)
# 内容完整性评
completeness_score = self.assess_content_completeness(content)
# 计算内容质量分数
content_quality['score'] = (
length_score * 0.2 +
depth_score * 0.3 +
originality_score * 0.25 +
accuracy_score * 0.15 +
completeness_score * 0.1
)
# 分析优势和劣
content_quality['strengths'] = self.identify_content_strengths(content)
content_quality['weaknesses'] = self.identify_content_weaknesses(content)
return content_quality
def assess_content_depth(self, content):
"""
评估内容深度
"""
depth_indicators = [
'分析', '解读', '解释', '说明', '探讨', '研究',
'对比', '比较', '评估', '预测', '展望', '建议'
]
depth_score = 0
for indicator in depth_indicators:
if indicator in content:
depth_score += 1
# 标准化分
max_depth_score = len(depth_indicators)
normalized_depth_score = depth_score / max_depth_score if max_depth_score > 0 else 0
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