diff --git a/src/landppt/services/image/matching/image_matcher.py b/src/landppt/services/image/matching/image_matcher.py
new file mode 100644
index 0000000..428b65e
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+++ b/src/landppt/services/image/matching/image_matcher.py
@@ -0,0 +1,620 @@
+"""
+智能图片匹配算法
+"""
+
+import asyncio
+import logging
+import re
+from typing import List, Dict, Any, Optional, Tuple
+import math
+from collections import Counter
+
+from ..models import ImageInfo, ImageTag
+
+logger = logging.getLogger(__name__)
+
+
+class ImageMatcher:
+    """智能图片匹配器"""
+
+    def __init__(self, config: Dict[str, Any]):
+        self.config = config
+
+        # 匹配权重配置
+        self.weights = {
+            'keyword_match': config.get('keyword_weight', 0.4),
+            'tag_match': config.get('tag_weight', 0.3),
+            'description_match': config.get('description_weight', 0.2),
+            'usage_popularity': config.get('usage_weight', 0.1)
+        }
+
+        # 停用词列表
+        self.stop_words = set([
+            '的', '了', '在', '是', '我', '有', '和', '就', '不', '人', '都', '一', '一个',
+            '上', '也', '很', '到', '说', '要', '去', '你', '会', '着', '没有', '看', '好',
+            'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of',
+            'with', 'by', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have',
+            'has', 'had', 'do', 'does', 'did', 'will', 'would', 'could', 'should'
+        ])
+
+    async def rank_images(self, query: str, images: List[ImageInfo]) -> List[ImageInfo]:
+        """对图片进行智能排序"""
+        if not images:
+            return images
+
+        try:
+            # 提取查询关键词
+            query_keywords = self._extract_keywords(query)
+
+            # 计算每个图片的匹配分数
+            scored_images = []
+            for image in images:
+                score = await self._calculate_match_score(query_keywords, image)
+                scored_images.append((score, image))
+
+            # 按分数排序
+            scored_images.sort(key=lambda x: x[0], reverse=True)
+
+            # 返回排序后的图片列表
+            return [image for _, image in scored_images]
+
+        except Exception as e:
+            logger.error(f"Failed to rank images: {e}")
+            return images
+
+    async def _calculate_match_score(self, query_keywords: List[str], image: ImageInfo) -> float:
+        """计算图片匹配分数"""
+        total_score = 0.0
+
+        try:
+            # 1. 关键词匹配分数
+            keyword_score = self._calculate_keyword_score(query_keywords, image)
+            total_score += keyword_score * self.weights['keyword_match']
+
+            # 2. 标签匹配分数
+            tag_score = self._calculate_tag_score(query_keywords, image)
+            total_score += tag_score * self.weights['tag_match']
+
+            # 3. 描述匹配分数
+            description_score = self._calculate_description_score(query_keywords, image)
+            total_score += description_score * self.weights['description_match']
+
+            # 4. 使用热度分数
+            popularity_score = self._calculate_popularity_score(image)
+            total_score += popularity_score * self.weights['usage_popularity']
+
+            return total_score
+
+        except Exception as e:
+            logger.error(f"Failed to calculate match score for image {image.image_id}: {e}")
+            return 0.0
+
+    def _extract_keywords(self, text: str) -> List[str]:
+        """提取关键词"""
+        if not text:
+            return []
+
+        # 转换为小写
+        text = text.lower()
+
+        # 使用正则表达式提取单词
+        words = re.findall(r'\b\w+\b', text)
+
+        # 过滤停用词和短词
+        keywords = [
+            word for word in words
+            if word not in self.stop_words and len(word) > 1
+        ]
+
+        return keywords
+
+    def _calculate_keyword_score(self, query_keywords: List[str], image: ImageInfo) -> float:
+        """计算关键词匹配分数"""
+        if not query_keywords:
+            return 0.0
+
+        # 获取图片的所有关键词
+        image_keywords = [kw.lower() for kw in image.keywords]
+
+        # 计算匹配的关键词数量
+        matches = 0
+        for query_kw in query_keywords:
+            for image_kw in image_keywords:
+                if query_kw in image_kw or image_kw in query_kw:
+                    matches += 1
+                    break
+
+        # 计算匹配率
+        match_ratio = matches / len(query_keywords)
+
+        return match_ratio
+
+    def _calculate_tag_score(self, query_keywords: List[str], image: ImageInfo) -> float:
+        """计算标签匹配分数"""
+        if not query_keywords or not image.tags:
+            return 0.0
+
+        # 计算匹配分数，考虑标签置信度
+        total_score = 0.0
+        total_weight = 0.0
+
+        for tag in image.tags:
+            tag_name = tag.name.lower()
+            tag_confidence = tag.confidence
+
+            # 检查标签是否与查询关键词匹配
+            match_score = 0.0
+            for query_kw in query_keywords:
+                if query_kw in tag_name or tag_name in query_kw:
+                    match_score = 1.0
+                    break
+                elif self._calculate_similarity(query_kw, tag_name) > 0.7:
+                    match_score = 0.8
+
+            total_score += match_score * tag_confidence
+            total_weight += tag_confidence
+
+        return total_score / total_weight if total_weight > 0 else 0.0
+
+    def _calculate_description_score(self, query_keywords: List[str], image: ImageInfo) -> float:
+        """计算描述匹配分数"""
+        if not query_keywords:
+            return 0.0
+
+        # 合并标题、描述和alt文本
+        text_content = []
+        if image.title:
+            text_content.append(image.title)
+        if image.description:
+            text_content.append(image.description)
+        if image.alt_text:
+            text_content.append(image.alt_text)
+
+        if not text_content:
+            return 0.0
+
+        # 提取描述中的关键词
+        description_text = ' '.join(text_content).lower()
+        description_keywords = self._extract_keywords(description_text)
+
+        # 计算TF-IDF相似度
+        return self._calculate_tfidf_similarity(query_keywords, description_keywords)
+
+    def _calculate_popularity_score(self, image: ImageInfo) -> float:
+        """计算使用热度分数"""
+        # 基于使用次数和最近使用时间计算热度
+        usage_count = image.usage_count
+
+        # 使用对数缩放避免热门图片过度占优
+        usage_score = math.log(usage_count + 1) / math.log(100)  # 归一化到0-1
+
+        return min(usage_score, 1.0)
+
+    def _calculate_similarity(self, word1: str, word2: str) -> float:
+        """计算两个词的相似度（简单的编辑距离）"""
+        if not word1 or not word2:
+            return 0.0
+
+        # 计算编辑距离
+        m, n = len(word1), len(word2)
+        dp = [[0] * (n + 1) for _ in range(m + 1)]
+
+        for i in range(m + 1):
+            dp[i][0] = i
+        for j in range(n + 1):
+            dp[0][j] = j
+
+        for i in range(1, m + 1):
+            for j in range(1, n + 1):
+                if word1[i-1] == word2[j-1]:
+                    dp[i][j] = dp[i-1][j-1]
+                else:
+                    dp[i][j] = min(dp[i-1][j], dp[i][j-1], dp[i-1][j-1]) + 1
+
+        # 转换为相似度
+        max_len = max(m, n)
+        similarity = 1.0 - (dp[m][n] / max_len) if max_len > 0 else 0.0
+
+        return similarity
+
+    def _calculate_tfidf_similarity(self, query_keywords: List[str], doc_keywords: List[str]) -> float:
+        """计算TF-IDF相似度"""
+        if not query_keywords or not doc_keywords:
+            return 0.0
+
+        # 简化的TF-IDF计算
+        query_counter = Counter(query_keywords)
+        doc_counter = Counter(doc_keywords)
+
+        # 计算交集
+        common_keywords = set(query_keywords) & set(doc_keywords)
+
+        if not common_keywords:
+            return 0.0
+
+        # 计算相似度
+        similarity = 0.0
+        for keyword in common_keywords:
+            query_tf = query_counter[keyword] / len(query_keywords)
+            doc_tf = doc_counter[keyword] / len(doc_keywords)
+            similarity += query_tf * doc_tf
+
+        return similarity
+
+    async def suggest_images_for_content(self,
+                                       content: str,
+                                       available_images: List[ImageInfo],
+                                       max_suggestions: int = 5) -> List[ImageInfo]:
+        """为内容推荐图片"""
+        try:
+            # 分析内容，提取关键信息
+            content_keywords = self._extract_keywords(content)
+
+            # 识别内容类型和主题
+            content_type = self._identify_content_type(content)
+            content_theme = self._identify_content_theme(content)
+
+            # 过滤相关图片
+            relevant_images = []
+            for image in available_images:
+                relevance_score = await self._calculate_content_relevance(
+                    content_keywords, content_type, content_theme, image
+                )
+                if relevance_score > 0.1:  # 设置最低相关度阈值
+                    relevant_images.append((relevance_score, image))
+
+            # 排序并返回前N个
+            relevant_images.sort(key=lambda x: x[0], reverse=True)
+
+            return [image for _, image in relevant_images[:max_suggestions]]
+
+        except Exception as e:
+            logger.error(f"Failed to suggest images for content: {e}")
+            return []
+
+    def _identify_content_type(self, content: str) -> str:
+        """识别内容类型"""
+        content_lower = content.lower()
+
+        # 简单的关键词匹配
+        if any(word in content_lower for word in ['数据', '统计', '图表', '分析', 'data', 'chart', 'graph']):
+            return 'data'
+        elif any(word in content_lower for word in ['技术', '科技', '创新', 'technology', 'innovation']):
+            return 'technology'
+        elif any(word in content_lower for word in ['商业', '业务', '市场', 'business', 'market']):
+            return 'business'
+        elif any(word in content_lower for word in ['教育', '学习', '培训', 'education', 'learning']):
+            return 'education'
+        else:
+            return 'general'
+
+    def _identify_content_theme(self, content: str) -> str:
+        """识别内容主题"""
+        content_lower = content.lower()
+
+        # 主题关键词映射
+        themes = {
+            'success': ['成功', '成就', '胜利', 'success', 'achievement', 'victory'],
+            'growth': ['增长', '发展', '提升', 'growth', 'development', 'improvement'],
+            'teamwork': ['团队', '合作', '协作', 'team', 'cooperation', 'collaboration'],
+            'innovation': ['创新', '创意', '新颖', 'innovation', 'creative', 'novel'],
+            'challenge': ['挑战', '困难', '问题', 'challenge', 'difficulty', 'problem'],
+            'future': ['未来', '前景', '展望', 'future', 'prospect', 'outlook']
+        }
+
+        for theme, keywords in themes.items():
+            if any(keyword in content_lower for keyword in keywords):
+                return theme
+
+        return 'neutral'
+
+    async def _calculate_content_relevance(self,
+                                         content_keywords: List[str],
+                                         content_type: str,
+                                         content_theme: str,
+                                         image: ImageInfo) -> float:
+        """计算图片与内容的相关度"""
+        relevance_score = 0.0
+
+        # 基础关键词匹配
+        keyword_score = self._calculate_keyword_score(content_keywords, image)
+        relevance_score += keyword_score * 0.4
+
+        # 标签匹配
+        tag_score = self._calculate_tag_score(content_keywords, image)
+        relevance_score += tag_score * 0.3
+
+        # 内容类型匹配
+        type_score = self._calculate_type_match(content_type, image)
+        relevance_score += type_score * 0.2
+
+        # 主题匹配
+        theme_score = self._calculate_theme_match(content_theme, image)
+        relevance_score += theme_score * 0.1
+
+        return relevance_score
+
+    def _calculate_type_match(self, content_type: str, image: ImageInfo) -> float:
+        """计算内容类型匹配度"""
+        # 根据图片标签判断类型匹配
+        type_keywords = {
+            'data': ['chart', 'graph', 'data', 'statistics', '图表', '数据'],
+            'technology': ['tech', 'computer', 'digital', '科技', '技术'],
+            'business': ['business', 'office', 'meeting', '商业', '办公'],
+            'education': ['education', 'learning', 'book', '教育', '学习']
+        }
+
+        if content_type not in type_keywords:
+            return 0.5  # 中性分数
+
+        type_words = type_keywords[content_type]
+        image_tags = [tag.name.lower() for tag in image.tags]
+        image_keywords = [kw.lower() for kw in image.keywords]
+
+        matches = 0
+        total_checks = len(type_words)
+
+        for word in type_words:
+            if any(word in tag for tag in image_tags) or any(word in kw for kw in image_keywords):
+                matches += 1
+
+        return matches / total_checks if total_checks > 0 else 0.0
+
+    def _calculate_theme_match(self, content_theme: str, image: ImageInfo) -> float:
+        """计算主题匹配度"""
+        # 根据图片的情感色彩和主题标签判断
+        theme_keywords = {
+            'success': ['success', 'winner', 'achievement', '成功', '胜利'],
+            'growth': ['growth', 'arrow', 'up', '增长', '上升'],
+            'teamwork': ['team', 'group', 'together', '团队', '合作'],
+            'innovation': ['innovation', 'creative', 'new', '创新', '创意'],
+            'challenge': ['challenge', 'difficult', 'problem', '挑战', '困难'],
+            'future': ['future', 'tomorrow', 'next', '未来', '明天']
+        }
+
+        if content_theme not in theme_keywords:
+            return 0.5  # 中性分数
+
+        theme_words = theme_keywords[content_theme]
+        image_tags = [tag.name.lower() for tag in image.tags]
+        image_description = (image.description or '').lower()
+
+        matches = 0
+        for word in theme_words:
+            if any(word in tag for tag in image_tags) or word in image_description:
+                matches += 1
+
+        return min(matches / len(theme_words), 1.0) if theme_words else 0.0
+    
+    def _calculate_description_score(self, query_keywords: List[str], image: ImageInfo) -> float:
+        """计算描述匹配分数"""
+        if not query_keywords:
+            return 0.0
+        
+        # 合并标题、描述和alt文本
+        text_content = []
+        if image.title:
+            text_content.append(image.title)
+        if image.description:
+            text_content.append(image.description)
+        if image.alt_text:
+            text_content.append(image.alt_text)
+        
+        if not text_content:
+            return 0.0
+        
+        # 提取描述中的关键词
+        description_text = ' '.join(text_content).lower()
+        description_keywords = self._extract_keywords(description_text)
+        
+        # 计算TF-IDF相似度
+        return self._calculate_tfidf_similarity(query_keywords, description_keywords)
+    
+    def _calculate_popularity_score(self, image: ImageInfo) -> float:
+        """计算使用热度分数"""
+        # 基于使用次数和最近使用时间计算热度
+        usage_count = image.usage_count
+        
+        # 使用对数缩放避免热门图片过度占优
+        usage_score = math.log(usage_count + 1) / math.log(100)  # 归一化到0-1
+        
+        # 考虑最近使用时间（可选）
+        # 这里简化处理，只考虑使用次数
+        
+        return min(usage_score, 1.0)
+    
+    def _calculate_similarity(self, word1: str, word2: str) -> float:
+        """计算两个词的相似度（简单的编辑距离）"""
+        if not word1 or not word2:
+            return 0.0
+        
+        # 计算编辑距离
+        m, n = len(word1), len(word2)
+        dp = [[0] * (n + 1) for _ in range(m + 1)]
+        
+        for i in range(m + 1):
+            dp[i][0] = i
+        for j in range(n + 1):
+            dp[0][j] = j
+        
+        for i in range(1, m + 1):
+            for j in range(1, n + 1):
+                if word1[i-1] == word2[j-1]:
+                    dp[i][j] = dp[i-1][j-1]
+                else:
+                    dp[i][j] = min(dp[i-1][j], dp[i][j-1], dp[i-1][j-1]) + 1
+        
+        # 转换为相似度
+        max_len = max(m, n)
+        similarity = 1.0 - (dp[m][n] / max_len) if max_len > 0 else 0.0
+        
+        return similarity
+    
+    def _calculate_tfidf_similarity(self, query_keywords: List[str], doc_keywords: List[str]) -> float:
+        """计算TF-IDF相似度"""
+        if not query_keywords or not doc_keywords:
+            return 0.0
+        
+        # 简化的TF-IDF计算
+        query_counter = Counter(query_keywords)
+        doc_counter = Counter(doc_keywords)
+        
+        # 计算交集
+        common_keywords = set(query_keywords) & set(doc_keywords)
+        
+        if not common_keywords:
+            return 0.0
+        
+        # 计算相似度
+        similarity = 0.0
+        for keyword in common_keywords:
+            query_tf = query_counter[keyword] / len(query_keywords)
+            doc_tf = doc_counter[keyword] / len(doc_keywords)
+            similarity += query_tf * doc_tf
+        
+        return similarity
+    
+    async def suggest_images_for_content(self, 
+                                       content: str, 
+                                       available_images: List[ImageInfo],
+                                       max_suggestions: int = 5) -> List[ImageInfo]:
+        """为内容推荐图片"""
+        try:
+            # 分析内容，提取关键信息
+            content_keywords = self._extract_keywords(content)
+            
+            # 识别内容类型和主题
+            content_type = self._identify_content_type(content)
+            content_theme = self._identify_content_theme(content)
+            
+            # 过滤相关图片
+            relevant_images = []
+            for image in available_images:
+                relevance_score = await self._calculate_content_relevance(
+                    content_keywords, content_type, content_theme, image
+                )
+                if relevance_score > 0.1:  # 设置最低相关度阈值
+                    relevant_images.append((relevance_score, image))
+            
+            # 排序并返回前N个
+            relevant_images.sort(key=lambda x: x[0], reverse=True)
+            
+            return [image for _, image in relevant_images[:max_suggestions]]
+            
+        except Exception as e:
+            logger.error(f"Failed to suggest images for content: {e}")
+            return []
+    
+    def _identify_content_type(self, content: str) -> str:
+        """识别内容类型"""
+        content_lower = content.lower()
+        
+        # 简单的关键词匹配
+        if any(word in content_lower for word in ['数据', '统计', '图表', '分析', 'data', 'chart', 'graph']):
+            return 'data'
+        elif any(word in content_lower for word in ['技术', '科技', '创新', 'technology', 'innovation']):
+            return 'technology'
+        elif any(word in content_lower for word in ['商业', '业务', '市场', 'business', 'market']):
+            return 'business'
+        elif any(word in content_lower for word in ['教育', '学习', '培训', 'education', 'learning']):
+            return 'education'
+        else:
+            return 'general'
+    
+    def _identify_content_theme(self, content: str) -> str:
+        """识别内容主题"""
+        content_lower = content.lower()
+        
+        # 主题关键词映射
+        themes = {
+            'success': ['成功', '成就', '胜利', 'success', 'achievement', 'victory'],
+            'growth': ['增长', '发展', '提升', 'growth', 'development', 'improvement'],
+            'teamwork': ['团队', '合作', '协作', 'team', 'cooperation', 'collaboration'],
+            'innovation': ['创新', '创意', '新颖', 'innovation', 'creative', 'novel'],
+            'challenge': ['挑战', '困难', '问题', 'challenge', 'difficulty', 'problem'],
+            'future': ['未来', '前景', '展望', 'future', 'prospect', 'outlook']
+        }
+        
+        for theme, keywords in themes.items():
+            if any(keyword in content_lower for keyword in keywords):
+                return theme
+        
+        return 'neutral'
+    
+    async def _calculate_content_relevance(self, 
+                                         content_keywords: List[str],
+                                         content_type: str,
+                                         content_theme: str,
+                                         image: ImageInfo) -> float:
+        """计算图片与内容的相关度"""
+        relevance_score = 0.0
+        
+        # 基础关键词匹配
+        keyword_score = self._calculate_keyword_score(content_keywords, image)
+        relevance_score += keyword_score * 0.4
+        
+        # 标签匹配
+        tag_score = self._calculate_tag_score(content_keywords, image)
+        relevance_score += tag_score * 0.3
+        
+        # 内容类型匹配
+        type_score = self._calculate_type_match(content_type, image)
+        relevance_score += type_score * 0.2
+        
+        # 主题匹配
+        theme_score = self._calculate_theme_match(content_theme, image)
+        relevance_score += theme_score * 0.1
+        
+        return relevance_score
+    
+    def _calculate_type_match(self, content_type: str, image: ImageInfo) -> float:
+        """计算内容类型匹配度"""
+        # 根据图片标签判断类型匹配
+        type_keywords = {
+            'data': ['chart', 'graph', 'data', 'statistics', '图表', '数据'],
+            'technology': ['tech', 'computer', 'digital', '科技', '技术'],
+            'business': ['business', 'office', 'meeting', '商业', '办公'],
+            'education': ['education', 'learning', 'book', '教育', '学习']
+        }
+        
+        if content_type not in type_keywords:
+            return 0.5  # 中性分数
+        
+        type_words = type_keywords[content_type]
+        image_tags = [tag.name.lower() for tag in image.tags]
+        image_keywords = [kw.lower() for kw in image.keywords]
+        
+        matches = 0
+        total_checks = len(type_words)
+        
+        for word in type_words:
+            if any(word in tag for tag in image_tags) or any(word in kw for kw in image_keywords):
+                matches += 1
+        
+        return matches / total_checks if total_checks > 0 else 0.0
+    
+    def _calculate_theme_match(self, content_theme: str, image: ImageInfo) -> float:
+        """计算主题匹配度"""
+        # 根据图片的情感色彩和主题标签判断
+        theme_keywords = {
+            'success': ['success', 'winner', 'achievement', '成功', '胜利'],
+            'growth': ['growth', 'arrow', 'up', '增长', '上升'],
+            'teamwork': ['team', 'group', 'together', '团队', '合作'],
+            'innovation': ['innovation', 'creative', 'new', '创新', '创意'],
+            'challenge': ['challenge', 'difficult', 'problem', '挑战', '困难'],
+            'future': ['future', 'tomorrow', 'next', '未来', '明天']
+        }
+        
+        if content_theme not in theme_keywords:
+            return 0.5  # 中性分数
+        
+        theme_words = theme_keywords[content_theme]
+        image_tags = [tag.name.lower() for tag in image.tags]
+        image_description = (image.description or '').lower()
+        
+        matches = 0
+        for word in theme_words:
+            if any(word in tag for tag in image_tags) or word in image_description:
+                matches += 1
+        
+        return min(matches / len(theme_words), 1.0) if theme_words else 0.0