Python实现繁体转简体功能的三种方案

import opencc
def convert_traditional_to_simplified_opencc(text):
    """
    使用OpenCC进行繁体转简体
    
    Args:
        text (str): 繁体中文文本
        
    Returns:
        str: 简体中文文本
    """
    converter = opencc.OpenCC('t2s.json')  # t2s表示繁体转简体
    return converter.convert(text)
# 示例使用
traditional_text = "繁體中文轉換為簡體中文"
simplified_text = convert_traditional_to_simplified_opencc(traditional_text)
print(f"繁体：{traditional_text}")
print(f"简体：{simplified_text}")

import zhconv
def convert_traditional_to_simplified_zhconv(text):
    """
    使用zhconv进行繁体转简体
    
    Args:
        text (str): 繁体中文文本
        
    Returns:
        str: 简体中文文本
    """
    return zhconv.convert(text, 'zh-cn')
# 示例使用
traditional_text = "學習繁體轉簡體的方法"
simplified_text = convert_traditional_to_simplified_zhconv(traditional_text)
print(f"繁体：{traditional_text}")
print(f"简体：{simplified_text}")

def create_traditional_simplified_dict():
    """
    创建繁简体字符映射字典
    这里只展示部分字符，实际应用中需要更完整的字典
    """
    return {
        '繁': '繁', '體': '体', '學': '学', '習': '习',
        '轉': '转', '換': '换', '為': '为', '簡': '简',
        '語': '语', '言': '言', '處': '处', '理': '理',
        '電': '电', '腦': '脑', '網': '网', '頁': '页',
        '開': '开', '發': '发', '應': '应', '用': '用'
    }
def convert_traditional_to_simplified_custom(text):
    """
    使用自定义字典进行繁体转简体
    
    Args:
        text (str): 繁体中文文本
        
    Returns:
        str: 简体中文文本
    """
    mapping = create_traditional_simplified_dict()
    result = []
    for char in text:
        result.append(mapping.get(char, char))
    return ''.join(result)
# 示例使用
traditional_text = "網頁開發應用程式"
simplified_text = convert_traditional_to_simplified_custom(traditional_text)
print(f"繁体：{traditional_text}")
print(f"简体：{simplified_text}")

import re
from typing import List, Dict
class ChineseTextProcessor:
    """中文文本处理器，支持繁简转换等功能"""
    
    def __init__(self):
        self.setup_converter()
    
    def setup_converter(self):
        """初始化转换器"""
        try:
            import opencc
            self.converter = opencc.OpenCC('t2s.json')
            self.conversion_method = 'opencc'
        except ImportError:
            try:
                import zhconv
                self.converter = zhconv
                self.conversion_method = 'zhconv'
            except ImportError:
                self.converter = None
                self.conversion_method = None
    
    def traditional_to_simplified(self, text: str) -> str:
        """
        繁体转简体
        
        Args:
            text: 输入文本
            
        Returns:
            转换后的简体文本
        """
        if not self.converter:
            return self._fallback_conversion(text)
        
        if self.conversion_method == 'opencc':
            return self.converter.convert(text)
        elif self.conversion_method == 'zhconv':
            return self.converter.convert(text, 'zh-cn')
        else:
            return text
    
    def _fallback_conversion(self, text: str) -> str:
        """备用转换方法"""
        basic_mapping = {
            '繁': '繁', '體': '体', '學': '学', '習': '习',
            '轉': '转', '換': '换', '為': '为', '簡': '简'
        }
        return ''.join(basic_mapping.get(char, char) for char in text)
    
    def batch_convert(self, texts: List[str]) -> List[str]:
        """批量转换文本"""
        return [self.traditional_to_simplified(text) for text in texts]
    
    def process_file(self, input_file: str, output_file: str):
        """处理文件中的文本"""
        try:
            with open(input_file, 'r', encoding='utf-8') as f:
                content = f.read()
            
            converted_content = self.traditional_to_simplified(content)
            
            with open(output_file, 'w', encoding='utf-8') as f:
                f.write(converted_content)
            
            print(f"文件转换完成：{input_file} -> {output_file}")
        except Exception as e:
            print(f"文件处理错误：{e}")
# 使用示例
def demonstrate_usage():
    """演示使用方法"""
    processor = ChineseTextProcessor()
    
    # 示例文本
    sample_texts = [
        "繁體中文轉換工具",
        "學習Python程式設計",
        "網頁開發與數據處理",
        "人工智慧與機器學習"
    ]
    
    print("=== 繁简转换示例 ===")
    for text in sample_texts:
        simplified = processor.traditional_to_simplified(text)
        print(f"繁体：{text}")
        print(f"简体：{simplified}")
        print("-" * 30)
    
    # 批量转换
    print("\n=== 批量转换结果 ===")
    batch_results = processor.batch_convert(sample_texts)
    for original, converted in zip(sample_texts, batch_results):
        print(f"{original} -> {converted}")
if __name__ == "__main__":
    demonstrate_usage()

from functools import lru_cache
class CachedConverter:
    def __init__(self):
        self.cache = {}
    
    @lru_cache(maxsize=1000)
    def convert_with_cache(self, text: str) -> str:
        """带缓存的转换方法"""
        return self.traditional_to_simplified(text)

def batch_process_texts(texts: List[str], batch_size: int = 100) -> List[str]:
    """批量处理文本，提高效率"""
    results = []
    for i in range(0, len(texts), batch_size):
        batch = texts[i:i + batch_size]
        batch_results = [convert_text(text) for text in batch]
        results.extend(batch_results)
    return results

def contextual_conversion(text: str) -> str:
    """考虑上下文的转换"""
    # 示例：处理"發"和"髮"的不同转换
    context_rules = {
        '發財': '发财',
        '頭髮': '头发',
        '發展': '发展'
    }
    
    result = text
    for traditional, simplified in context_rules.items():
        result = result.replace(traditional, simplified)
    
    return result

def normalize_punctuation(text: str) -> str:
    """标准化标点符号"""
    punctuation_map = {
        '，': ',',  # 全角逗号转半角
        '。': '.',  # 全角句号转半角
        '！': '!',  # 全角感叹号转半角
        '？': '?',  # 全角问号转半角
        '：': ':',  # 全角冒号转半角
        '；': ';',  # 全角分号转半角
    }
    
    for trad_punct, simp_punct in punctuation_map.items():
        text = text.replace(trad_punct, simp_punct)
    
    return text