diff --git a/frontend/src/components/StockAnalysisApp.vue b/frontend/src/components/StockAnalysisApp.vue
index e852e01..4b580a0 100644
--- a/frontend/src/components/StockAnalysisApp.vue
+++ b/frontend/src/components/StockAnalysisApp.vue
@@ -76,6 +76,45 @@
             <!-- 右侧结果区域 -->
             <n-grid-item :span="24" :lg-span="16">
               <div class="results-section">
+                <div class="results-header">
+                  <n-space align="center" justify="space-between">
+                    <n-text>分析结果 ({{ analyzedStocks.length }})</n-text>
+                    <n-space>
+                      <n-select 
+                        v-model:value="displayMode" 
+                        size="small" 
+                        style="width: 120px"
+                        :options="[
+                          { label: '卡片视图', value: 'card' },
+                          { label: '表格视图', value: 'table' }
+                        ]"
+                      />
+                      <n-button 
+                        size="small" 
+                        :disabled="analyzedStocks.length === 0"
+                        @click="copyAnalysisResults"
+                      >
+                        复制结果
+                      </n-button>
+                      <n-dropdown 
+                        trigger="click" 
+                        :disabled="analyzedStocks.length === 0"
+                        :options="exportOptions"
+                        @select="handleExportSelect"
+                      >
+                        <n-button size="small" :disabled="analyzedStocks.length === 0">
+                          导出
+                          <template #icon>
+                            <n-icon>
+                              <DownloadIcon />
+                            </n-icon>
+                          </template>
+                        </n-button>
+                      </n-dropdown>
+                    </n-space>
+                  </n-space>
+                </div>
+                
                 <template v-if="analyzedStocks.length === 0 && !isAnalyzing">
                   <n-empty description="尚未分析股票" size="large">
                     <template #icon>
@@ -84,13 +123,25 @@
                   </n-empty>
                 </template>
                 
-                <template v-else>
+                <template v-else-if="displayMode === 'card'">
                   <n-grid :cols="1" :x-gap="16" :y-gap="16" :lg-cols="2">
                     <n-grid-item v-for="stock in analyzedStocks" :key="stock.code">
                       <StockCard :stock="stock" />
                     </n-grid-item>
                   </n-grid>
                 </template>
+                
+                <template v-else>
+                  <n-data-table
+                    :columns="stockTableColumns"
+                    :data="analyzedStocks"
+                    :pagination="{ pageSize: 10 }"
+                    :row-key="(row) => row.code"
+                    :bordered="false"
+                    :single-line="false"
+                    striped
+                  />
+                </template>
               </div>
             </n-grid-item>
           </n-grid>
@@ -101,7 +152,7 @@
 </template>
 
 <script setup lang="ts">
-import { ref, onMounted } from 'vue';
+import { ref, onMounted, onBeforeUnmount } from 'vue';
 import { 
   NLayout, 
   NLayoutContent, 
@@ -115,12 +166,18 @@ import {
   NInput, 
   NButton,
   NEmpty,
-  useMessage
+  useMessage,
+  NSpace,
+  NText,
+  NDataTable,
+  NDropdown,
+  type DataTableColumns
 } from 'naive-ui';
 import { useClipboard } from '@vueuse/core'
 import { 
-  BarChart as BarChartIcon,
-  DocumentText as DocumentTextIcon
+  BarChartOutline as BarChartIcon,
+  DocumentTextOutline as DocumentTextIcon,
+  DownloadOutline as DownloadIcon
 } from '@vicons/ionicons5';
 
 import AnnouncementBanner from './AnnouncementBanner.vue';
@@ -148,6 +205,7 @@ const marketType = ref('A');
 const stockCodes = ref('');
 const isAnalyzing = ref(false);
 const analyzedStocks = ref<StockInfo[]>([]);
+const displayMode = ref<'card' | 'table'>('card');
 
 // API配置
 const apiConfig = ref<ApiConfig>({
@@ -167,6 +225,136 @@ const marketOptions = [
   { label: 'LOF', value: 'LOF' }
 ];
 
+// 表格列定义
+const stockTableColumns = ref<DataTableColumns<StockInfo>>([
+  {
+    title: '代码',
+    key: 'code',
+    width: 100,
+    fixed: 'left'
+  },
+  {
+    title: '状态',
+    key: 'analysisStatus',
+    width: 100,
+    render(row: StockInfo) {
+      const statusMap = {
+        'waiting': '等待分析',
+        'analyzing': '分析中',
+        'completed': '已完成',
+        'error': '出错'
+      };
+      return statusMap[row.analysisStatus] || row.analysisStatus;
+    }
+  },
+  {
+    title: '价格',
+    key: 'price',
+    width: 100,
+    render(row: StockInfo) {
+      return row.price !== undefined ? row.price.toFixed(2) : '--';
+    }
+  },
+  {
+    title: '涨跌幅',
+    key: 'changePercent',
+    width: 100,
+    render(row: StockInfo) {
+      if (row.changePercent === undefined) return '--';
+      const sign = row.changePercent > 0 ? '+' : '';
+      return `${sign}${row.changePercent.toFixed(2)}%`;
+    }
+  },
+  {
+    title: 'RSI',
+    key: 'rsi',
+    width: 80,
+    render(row: StockInfo) {
+      return row.rsi !== undefined ? row.rsi.toFixed(2) : '--';
+    }
+  },
+  {
+    title: '均线趋势',
+    key: 'ma_trend',
+    width: 100,
+    render(row: StockInfo) {
+      const trendMap: Record<string, string> = {
+        'UP': '上升',
+        'DOWN': '下降',
+        'NEUTRAL': '平稳'
+      };
+      return row.ma_trend ? trendMap[row.ma_trend] || row.ma_trend : '--';
+    }
+  },
+  {
+    title: 'MACD信号',
+    key: 'macd_signal',
+    width: 100,
+    render(row: StockInfo) {
+      const signalMap: Record<string, string> = {
+        'BUY': '买入',
+        'SELL': '卖出',
+        'HOLD': '持有',
+        'NEUTRAL': '中性'
+      };
+      return row.macd_signal ? signalMap[row.macd_signal] || row.macd_signal : '--';
+    }
+  },
+  {
+    title: '评分',
+    key: 'score',
+    width: 80,
+    render(row: StockInfo) {
+      return row.score !== undefined ? row.score : '--';
+    }
+  },
+  {
+    title: '推荐',
+    key: 'recommendation',
+    width: 100
+  },
+  {
+    title: '分析日期',
+    key: 'analysis_date',
+    width: 120,
+    render(row: StockInfo) {
+      if (!row.analysis_date) return '--';
+      try {
+        const date = new Date(row.analysis_date);
+        if (isNaN(date.getTime())) {
+          return row.analysis_date;
+        }
+        return date.toISOString().split('T')[0];
+      } catch (e) {
+        return row.analysis_date;
+      }
+    }
+  },
+  {
+    title: '分析结果',
+    key: 'analysis',
+    ellipsis: {
+      tooltip: true
+    }
+  }
+]);
+
+// 导出选项
+const exportOptions = [
+  {
+    label: '导出为CSV',
+    key: 'csv'
+  },
+  {
+    label: '导出为Excel',
+    key: 'excel'
+  },
+  {
+    label: '导出为PDF',
+    key: 'pdf'
+  }
+];
+
 // 更新API配置
 function updateApiConfig(config: ApiConfig) {
   apiConfig.value = { ...config };
@@ -200,6 +388,22 @@ function processStreamData(text: string) {
     } else if (data.stock_code) {
       // 更新消息
       handleStreamUpdate(data as StreamAnalysisUpdate);
+    } else if (data.scan_completed) {
+      // 扫描完成消息
+      message.success(`分析完成，共扫描 ${data.total_scanned} 只股票，符合条件 ${data.total_matched} 只`);
+      
+      // 将所有分析中的股票状态更新为已完成
+      analyzedStocks.value.forEach((stock, index) => {
+        if (stock.analysisStatus === 'analyzing') {
+          const updatedStock = { 
+            ...stock, 
+            analysisStatus: 'completed' as const 
+          };
+          analyzedStocks.value[index] = updatedStock;
+        }
+      });
+      
+      isAnalyzing.value = false;
     }
   } catch (e) {
     console.error('解析流数据出错:', e);
@@ -414,13 +618,7 @@ async function analyzeStocks() {
       processStreamData(buffer);
     }
     
-    // 将所有分析中的股票状态更新为已完成
-    analyzedStocks.value.forEach((stock, index) => {
-      if (stock.analysisStatus === 'analyzing') {
-        const updatedStock = { ...stock, analysisStatus: 'completed' };
-        analyzedStocks.value[index] = updatedStock;
-      }
-    });
+    // 注意：不再需要在这里更新状态，因为已经在processStreamData中处理了scan_completed消息
     
     message.success('分析完成');
   } catch (error: any) {
@@ -547,6 +745,108 @@ function restoreLocalApiConfig() {
   }
 }
 
+// 处理导出选择
+function handleExportSelect(key: string) {
+  switch (key) {
+    case 'csv':
+      exportToCSV();
+      break;
+    case 'excel':
+      message.info('Excel导出功能即将推出');
+      break;
+    case 'pdf':
+      message.info('PDF导出功能即将推出');
+      break;
+  }
+}
+
+// 导出为CSV
+function exportToCSV() {
+  if (analyzedStocks.value.length === 0) {
+    message.warning('没有可导出的分析结果');
+    return;
+  }
+  
+  try {
+    // 创建CSV内容
+    const headers = ['代码', '名称', '价格', '涨跌幅', 'RSI', '均线趋势', 'MACD信号', '成交量状态', '评分', '推荐', '分析日期'];
+    let csvContent = headers.join(',') + '\n';
+    
+    // 添加数据行
+    analyzedStocks.value.forEach(stock => {
+      const row = [
+        `"${stock.code}"`,
+        `"${stock.name || ''}"`,
+        stock.price !== undefined ? stock.price.toFixed(2) : '',
+        stock.changePercent !== undefined ? `${stock.changePercent > 0 ? '+' : ''}${stock.changePercent.toFixed(2)}%` : '',
+        stock.rsi !== undefined ? stock.rsi.toFixed(2) : '',
+        stock.ma_trend ? getChineseTrend(stock.ma_trend) : '',
+        stock.macd_signal ? getChineseSignal(stock.macd_signal) : '',
+        stock.volume_status ? getChineseVolumeStatus(stock.volume_status) : '',
+        stock.score !== undefined ? stock.score : '',
+        `"${stock.recommendation || ''}"`,
+        stock.analysis_date || ''
+      ];
+      
+      csvContent += row.join(',') + '\n';
+    });
+    
+    // 创建Blob对象
+    const blob = new Blob([csvContent], { type: 'text/csv;charset=utf-8;' });
+    const url = URL.createObjectURL(blob);
+    
+    // 创建下载链接
+    const link = document.createElement('a');
+    link.setAttribute('href', url);
+    link.setAttribute('download', `股票分析结果_${new Date().toISOString().split('T')[0]}.csv`);
+    link.style.visibility = 'hidden';
+    
+    // 添加到文档并触发点击
+    document.body.appendChild(link);
+    link.click();
+    
+    // 清理
+    document.body.removeChild(link);
+    URL.revokeObjectURL(url);
+    
+    message.success('已导出CSV文件');
+  } catch (error) {
+    message.error('导出失败');
+    console.error('导出CSV时出错:', error);
+  }
+}
+
+// 辅助函数：获取中文趋势描述
+function getChineseTrend(trend: string): string {
+  const trendMap: Record<string, string> = {
+    'UP': '上升',
+    'DOWN': '下降',
+    'NEUTRAL': '平稳'
+  };
+  return trendMap[trend] || trend;
+}
+
+// 辅助函数：获取中文信号描述
+function getChineseSignal(signal: string): string {
+  const signalMap: Record<string, string> = {
+    'BUY': '买入',
+    'SELL': '卖出',
+    'HOLD': '持有',
+    'NEUTRAL': '中性'
+  };
+  return signalMap[signal] || signal;
+}
+
+// 辅助函数：获取中文成交量状态描述
+function getChineseVolumeStatus(status: string): string {
+  const statusMap: Record<string, string> = {
+    'HIGH': '放量',
+    'LOW': '缩量',
+    'NORMAL': '正常'
+  };
+  return statusMap[status] || status;
+}
+
 // 页面加载时获取默认配置和公告
 onMounted(async () => {
   try {
@@ -615,4 +915,15 @@ onMounted(async () => {
   padding: 0.5rem;
   min-height: 200px;
 }
+
+.results-header {
+  margin-bottom: 1rem;
+}
+
+.n-data-table .analysis-cell {
+  max-width: 300px;
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+}
 </style>
diff --git a/frontend/src/components/StockCard.vue b/frontend/src/components/StockCard.vue
index 52673b7..aa610d9 100644
--- a/frontend/src/components/StockCard.vue
+++ b/frontend/src/components/StockCard.vue
@@ -536,6 +536,36 @@ function getChineseVolumeStatus(status: string): string {
   overflow-y: auto;
   word-break: break-word;
   hyphens: auto;
+  
+  /* 自定义滚动条样式 */
+  scrollbar-width: thin; /* Firefox */
+  scrollbar-color: rgba(32, 128, 240, 0.3) transparent; /* Firefox */
+}
+
+/* Webkit浏览器的滚动条样式 */
+.analysis-result::-webkit-scrollbar {
+  width: 6px;
+  height: 6px;
+}
+
+.analysis-result::-webkit-scrollbar-track {
+  background: transparent;
+  border-radius: 3px;
+}
+
+.analysis-result::-webkit-scrollbar-thumb {
+  background-color: rgba(32, 128, 240, 0.3);
+  border-radius: 3px;
+  transition: background-color 0.3s ease;
+}
+
+.analysis-result::-webkit-scrollbar-thumb:hover {
+  background-color: rgba(32, 128, 240, 0.5);
+}
+
+/* 在不滚动时隐藏滚动条，滚动时显示 */
+.analysis-result:not(:hover)::-webkit-scrollbar-thumb {
+  background-color: rgba(32, 128, 240, 0.1);
 }
 
 .analysis-streaming {
diff --git a/services/ai_analyzer.py b/services/ai_analyzer.py
index 73ba626..f7fa01e 100644
--- a/services/ai_analyzer.py
+++ b/services/ai_analyzer.py
@@ -4,10 +4,12 @@ import os
 import json
 import asyncio
 import httpx
+import re
 from typing import Dict, List, Optional, Any, Generator, AsyncGenerator
 from dotenv import load_dotenv
 from logger import get_logger
 from utils.api_utils import APIUtils
+from datetime import datetime
 
 # 获取日志器
 logger = get_logger()
@@ -55,6 +57,26 @@ class AIAnalyzer:
         try:
             logger.info(f"开始AI分析 {stock_code}, 流式模式: {stream}")
             
+            # 提取关键技术指标
+            latest_data = df.iloc[-1]
+            
+            # 计算技术指标
+            rsi = latest_data.get('RSI')
+            price = latest_data.get('Close')
+            price_change = latest_data.get('Change')
+            
+            # 确定MA趋势
+            ma_trend = 'UP' if latest_data.get('MA5', 0) > latest_data.get('MA20', 0) else 'DOWN'
+            
+            # 确定MACD信号
+            macd = latest_data.get('MACD', 0)
+            macd_signal = latest_data.get('MACD_Signal', 0)
+            macd_signal_type = 'BUY' if macd > macd_signal else 'SELL'
+            
+            # 确定成交量状态
+            volume_ratio = latest_data.get('Volume_Ratio', 1)
+            volume_status = 'HIGH' if volume_ratio > 1.5 else ('LOW' if volume_ratio < 0.5 else 'NORMAL')
+            
             # AI 分析内容
             # 最近14天的股票数据记录
             recent_data = df.tail(14).to_dict('records')
@@ -78,25 +100,19 @@ class AIAnalyzer:
                 近14日交易数据：
                 {recent_data}
                 
-                请分析该基金的技术面状况，包括：
-                1. 趋势分析：判断基金当前的趋势方向
-                2. 动量分析：基于RSI和交易量评估基金动量
-                3. 支撑与阻力位：确定关键价格位
-                4. 技术面总结
-                5. 投资建议
-
-                将分析结果格式化为JSON，像这样：
-                {{
-                    "trend_analysis": "趋势分析结果...",
-                    "momentum_analysis": "动量分析结果...",
-                    "support_resistance": "支撑阻力位分析...",
-                    "technical_summary": "技术面总结...",
-                    "investment_advice": "投资建议..."
-                }}
+                请提供：
+                1. 净值走势分析（包含支撑位和压力位）
+                2. 成交量分析及其对净值的影响
+                3. 风险评估（包含波动率和折溢价分析）
+                4. 短期和中期净值预测
+                5. 关键价格位分析
+                6. 申购赎回建议（包含止损位）
+                
+                请基于技术指标和市场表现进行分析，给出具体数据支持。
                 """
-            else:
+            elif market_type == 'US':
                 prompt = f"""
-                分析股票 {stock_code}：
+                分析美股 {stock_code}：
 
                 技术指标概要：
                 {technical_summary}
@@ -104,25 +120,55 @@ class AIAnalyzer:
                 近14日交易数据：
                 {recent_data}
                 
-                请分析该股票的技术面状况，包括：
-                1. 趋势分析：当前趋势方向及强度
-                2. 动量分析：基于MACD、RSI等指标
-                3. 支撑与阻力位：关键价格位分析
-                4. 成交量分析：交易量的变化及意义
-                5. 波动性评估：ATR和波动率分析
-                6. 技术面总结
-                7. 投资建议：根据技术分析给出操作建议
+                请提供：
+                1. 趋势分析（包含支撑位和压力位，美元计价）
+                2. 成交量分析及其含义
+                3. 风险评估（包含波动率和美股市场特有风险）
+                4. 短期和中期目标价位（美元）
+                5. 关键技术位分析
+                6. 具体交易建议（包含止损位）
+                
+                请基于技术指标和美股市场特点进行分析，给出具体数据支持。
+                """
+            elif market_type == 'HK':
+                prompt = f"""
+                分析港股 {stock_code}：
 
-                将分析结果格式化为JSON，像这样：
-                {{
-                    "trend_analysis": "趋势分析结果...",
-                    "momentum_analysis": "动量分析结果...",
-                    "support_resistance": "支撑阻力位分析...",
-                    "volume_analysis": "成交量分析...",
-                    "volatility_assessment": "波动性评估...",
-                    "technical_summary": "技术面总结...",
-                    "investment_advice": "投资建议..."
-                }}
+                技术指标概要：
+                {technical_summary}
+                
+                近14日交易数据：
+                {recent_data}
+                
+                请提供：
+                1. 趋势分析（包含支撑位和压力位，港币计价）
+                2. 成交量分析及其含义
+                3. 风险评估（包含波动率和港股市场特有风险）
+                4. 短期和中期目标价位（港币）
+                5. 关键技术位分析
+                6. 具体交易建议（包含止损位）
+                
+                请基于技术指标和港股市场特点进行分析，给出具体数据支持。
+                """
+            else:  # A股
+                prompt = f"""
+                分析A股 {stock_code}：
+
+                技术指标概要：
+                {technical_summary}
+                
+                近14日交易数据：
+                {recent_data}
+                
+                请提供：
+                1. 趋势分析（包含支撑位和压力位）
+                2. 成交量分析及其含义
+                3. 风险评估（包含波动率分析）
+                4. 短期和中期目标价位
+                5. 关键技术位分析
+                6. 具体交易建议（包含止损位）
+                
+                请基于技术指标和A股市场特点进行分析，给出具体数据支持。
                 """
             
             # 格式化API URL
@@ -142,11 +188,27 @@ class AIAnalyzer:
                 "Authorization": f"Bearer {self.API_KEY}"
             }
             
+            # 获取当前日期作为分析日期
+            analysis_date = datetime.now().strftime("%Y-%m-%d")
+            
             # 异步请求API
             async with httpx.AsyncClient(timeout=self.API_TIMEOUT) as client:
                 # 记录请求
                 logger.debug(f"发送AI请求: URL={api_url}, MODEL={self.API_MODEL}, STREAM={stream}")
                 
+                # 先发送技术指标数据
+                yield json.dumps({
+                    "stock_code": stock_code,
+                    "status": "analyzing",
+                    "rsi": rsi,
+                    "price": price,
+                    "price_change": price_change,
+                    "ma_trend": ma_trend,
+                    "macd_signal": macd_signal_type,
+                    "volume_status": volume_status,
+                    "analysis_date": analysis_date
+                })
+                
                 if stream:
                     # 流式响应处理
                     async with client.stream("POST", api_url, json=request_data, headers=headers) as response:
@@ -155,12 +217,17 @@ class AIAnalyzer:
                             error_data = json.loads(error_text)
                             error_message = error_data.get('error', {}).get('message', '未知错误')
                             logger.error(f"AI API请求失败: {response.status_code} - {error_message}")
-                            yield json.dumps({"error": f"API请求失败: {error_message}"})
+                            yield json.dumps({
+                                "stock_code": stock_code,
+                                "error": f"API请求失败: {error_message}",
+                                "status": "error"
+                            })
                             return
                             
                         # 处理流式响应
                         buffer = ""
                         collected_messages = []
+                        chunk_count = 0
                         
                         async for chunk in response.aiter_text():
                             if chunk:
@@ -169,6 +236,7 @@ class AIAnalyzer:
                                     chunk_str = chunk_str[6:]  # 去除"data: "前缀
                                     
                                 if chunk_str == "[DONE]":
+                                    logger.debug("收到流结束标记 [DONE]")
                                     continue
                                     
                                 try:
@@ -178,49 +246,48 @@ class AIAnalyzer:
                                     content = delta.get("content", "")
                                     
                                     if content:
+                                        chunk_count += 1
                                         buffer += content
-                                        # 尝试提取完整的JSON
-                                        if buffer.strip().startswith("{") and buffer.strip().endswith("}"):
-                                            try:
-                                                result_json = json.loads(buffer)
-                                                yield json.dumps({
-                                                    "stock_code": stock_code,
-                                                    "analysis": result_json
-                                                })
-                                                buffer = ""  # 重置缓冲区
-                                            except json.JSONDecodeError:
-                                                # JSON不完整，继续收集
-                                                pass
+                                        collected_messages.append(content)
                                         
-                                        # 达到一定长度就输出
-                                        if len(buffer) > 100:
-                                            yield json.dumps({
-                                                "stock_code": stock_code,
-                                                "ai_analysis_chunk": buffer
-                                            })
-                                            collected_messages.append(buffer)
-                                            buffer = ""
+                                        # 直接发送每个内容片段，不累积
+                                        yield json.dumps({
+                                            "stock_code": stock_code,
+                                            "ai_analysis_chunk": content,
+                                            "status": "analyzing"
+                                        })
                                 except json.JSONDecodeError:
                                     # 忽略无法解析的块
+                                    logger.error(f"JSON解析错误，块内容: {chunk_str[:100]}...")
                                     continue
                         
-                        # 处理最后的缓冲区
-                        if buffer:
+                        logger.info(f"AI流式处理完成，共收到 {chunk_count} 个内容片段，总长度: {len(buffer)}")
+                        
+                        # 如果buffer不为空且不以换行符结束，发送一个换行符
+                        if buffer and not buffer.endswith('\n'):
+                            logger.debug("发送换行符")
                             yield json.dumps({
                                 "stock_code": stock_code,
-                                "ai_analysis_chunk": buffer
+                                "ai_analysis_chunk": "\n",
+                                "status": "analyzing"
                             })
-                            collected_messages.append(buffer)
                         
-                        # 尝试从整个内容中提取JSON
-                        full_content = "".join(collected_messages)
+                        # 完整的分析内容
+                        full_content = buffer
                         
-                        # 如果没有成功解析JSON，返回原始内容
-                        if not full_content.strip().startswith("{"):
-                            yield json.dumps({
-                                "stock_code": stock_code,
-                                "raw_analysis": full_content
-                            })
+                        # 尝试从分析内容中提取投资建议
+                        recommendation = self._extract_recommendation(full_content)
+                        
+                        # 计算分析评分
+                        score = self._calculate_analysis_score(full_content, technical_summary)
+                        
+                        # 发送完成状态和评分、建议
+                        yield json.dumps({
+                            "stock_code": stock_code,
+                            "status": "completed",
+                            "score": score,
+                            "recommendation": recommendation
+                        })
                 else:
                     # 非流式响应处理
                     response = await client.post(api_url, json=request_data, headers=headers)
@@ -229,32 +296,100 @@ class AIAnalyzer:
                         error_data = response.json()
                         error_message = error_data.get('error', {}).get('message', '未知错误')
                         logger.error(f"AI API请求失败: {response.status_code} - {error_message}")
-                        yield json.dumps({"error": f"API请求失败: {error_message}"})
+                        yield json.dumps({
+                            "stock_code": stock_code,
+                            "error": f"API请求失败: {error_message}",
+                            "status": "error"
+                        })
                         return
                     
                     response_data = response.json()
                     analysis_text = response_data.get("choices", [{}])[0].get("message", {}).get("content", "")
                     
-                    try:
-                        # 尝试解析JSON
-                        analysis_json = json.loads(analysis_text)
-                        yield json.dumps({
-                            "stock_code": stock_code,
-                            "analysis": analysis_json
-                        })
-                    except json.JSONDecodeError:
-                        # 返回原始文本
-                        yield json.dumps({
-                            "stock_code": stock_code,
-                            "raw_analysis": analysis_text
-                        })
-                        
-            logger.info(f"完成对 {stock_code} 的AI分析")
-                
+                    # 尝试从分析内容中提取投资建议
+                    recommendation = self._extract_recommendation(analysis_text)
+                    
+                    # 计算分析评分
+                    score = self._calculate_analysis_score(analysis_text, technical_summary)
+                    
+                    # 发送完整的分析结果
+                    yield json.dumps({
+                        "stock_code": stock_code,
+                        "status": "completed",
+                        "analysis": analysis_text,
+                        "score": score,
+                        "recommendation": recommendation,
+                        "rsi": rsi,
+                        "price": price,
+                        "price_change": price_change,
+                        "ma_trend": ma_trend,
+                        "macd_signal": macd_signal_type,
+                        "volume_status": volume_status,
+                        "analysis_date": analysis_date
+                    })
+                    
         except Exception as e:
-            logger.error(f"AI分析 {stock_code} 时出错: {str(e)}")
-            logger.exception(e)
-            yield json.dumps({"error": f"分析出错: {str(e)}"})
+            logger.error(f"AI分析出错: {str(e)}", exc_info=True)
+            yield json.dumps({
+                "stock_code": stock_code,
+                "error": f"分析出错: {str(e)}",
+                "status": "error"
+            })
+            
+    def _extract_recommendation(self, analysis_text: str) -> str:
+        """从分析文本中提取投资建议"""
+        # 查找投资建议部分
+        investment_advice_pattern = r"##\s*投资建议\s*\n(.*?)(?:\n##|\Z)"
+        match = re.search(investment_advice_pattern, analysis_text, re.DOTALL)
+        
+        if match:
+            advice_text = match.group(1).strip()
+            
+            # 提取关键建议
+            if "买入" in advice_text or "增持" in advice_text:
+                return "买入"
+            elif "卖出" in advice_text or "减持" in advice_text:
+                return "卖出"
+            elif "持有" in advice_text:
+                return "持有"
+            else:
+                return "观望"
+        
+        return "观望"  # 默认建议
+        
+    def _calculate_analysis_score(self, analysis_text: str, technical_summary: dict) -> int:
+        """计算分析评分"""
+        score = 50  # 基础分数
+        
+        # 根据技术指标调整分数
+        if technical_summary['trend'] == 'upward':
+            score += 10
+        else:
+            score -= 10
+            
+        if technical_summary['volume_trend'] == 'increasing':
+            score += 5
+        else:
+            score -= 5
+            
+        rsi = technical_summary['rsi_level']
+        if rsi < 30:  # 超卖
+            score += 15
+        elif rsi > 70:  # 超买
+            score -= 15
+            
+        # 根据分析文本中的关键词调整分数
+        if "强烈买入" in analysis_text or "显著上涨" in analysis_text:
+            score += 20
+        elif "买入" in analysis_text or "看涨" in analysis_text:
+            score += 10
+        elif "强烈卖出" in analysis_text or "显著下跌" in analysis_text:
+            score -= 20
+        elif "卖出" in analysis_text or "看跌" in analysis_text:
+            score -= 10
+            
+        # 确保分数在0-100范围内
+        return max(0, min(100, score))
     
     def _truncate_json_for_logging(self, json_obj, max_length=500):
         """
diff --git a/services/stock_analyzer_service.py b/services/stock_analyzer_service.py
index 2e24a72..191663f 100644
--- a/services/stock_analyzer_service.py
+++ b/services/stock_analyzer_service.py
@@ -159,10 +159,10 @@ class StockAnalyzerService:
         try:
             logger.info(f"开始批量扫描 {len(stock_codes)} 只股票, 市场: {market_type}")
             
-            # 输出初始状态
+            # 输出初始状态 - 发送批量分析初始化消息
             yield json.dumps({
-                "status": "scanning",
-                "total_stocks": len(stock_codes),
+                "stream_type": "batch",
+                "stock_codes": stock_codes,
                 "market_type": market_type,
                 "min_score": min_score
             })
@@ -177,6 +177,12 @@ class StockAnalyzerService:
                     stock_with_indicators[code] = self.indicator.calculate_indicators(df)
                 except Exception as e:
                     logger.error(f"计算 {code} 技术指标时出错: {str(e)}")
+                    # 发送错误状态
+                    yield json.dumps({
+                        "stock_code": code,
+                        "error": f"计算技术指标时出错: {str(e)}",
+                        "status": "error"
+                    })
             
             # 评分股票
             results = self.scorer.batch_score_stocks(stock_with_indicators)
@@ -184,30 +190,39 @@ class StockAnalyzerService:
             # 过滤低于最低评分的股票
             filtered_results = [r for r in results if r[1] >= min_score]
             
-            # 输出评分结果
-            yield json.dumps({
-                "scan_results": [
-                    {
+            # 为每只股票发送基本评分和推荐信息
+            for code, score, rec in results:
+                df = stock_with_indicators.get(code)
+                if df is not None and len(df) > 0:
+                    # 获取最新数据
+                    latest_data = df.iloc[-1]
+                    
+                    # 发送股票基本信息和评分
+                    yield json.dumps({
                         "stock_code": code,
                         "score": score,
-                        "recommendation": rec
-                    } for code, score, rec in filtered_results
-                ],
-                "total_matched": len(filtered_results),
-                "total_scanned": len(results)
-            })
+                        "recommendation": rec,
+                        "price": float(latest_data.get('Close', 0)),
+                        "price_change": float(latest_data.get('Change', 0)),
+                        "rsi": float(latest_data.get('RSI', 0)) if 'RSI' in latest_data else None,
+                        "ma_trend": "UP" if latest_data.get('MA5', 0) > latest_data.get('MA20', 0) else "DOWN",
+                        "macd_signal": "BUY" if latest_data.get('MACD', 0) > latest_data.get('MACD_Signal', 0) else "SELL",
+                        "volume_status": "HIGH" if latest_data.get('Volume_Ratio', 1) > 1.5 else ("LOW" if latest_data.get('Volume_Ratio', 1) < 0.5 else "NORMAL"),
+                        "status": "completed" if score < min_score else "waiting"
+                    })
             
             # 如果需要进一步分析，对评分较高的股票进行AI分析
             if stream and filtered_results:
-                top_stocks = filtered_results[:3]  # 只分析前3只评分最高的股票
+                # 只分析前5只评分最高的股票，避免分析过多导致前端卡顿
+                top_stocks = filtered_results[:5]
                 
                 for stock_code, score, _ in top_stocks:
                     df = stock_with_indicators.get(stock_code)
                     if df is not None:
                         # 输出正在分析的股票信息
                         yield json.dumps({
-                            "analyzing": stock_code,
-                            "score": score
+                            "stock_code": stock_code,
+                            "status": "analyzing"
                         })
                         
                         # AI分析
@@ -216,7 +231,7 @@ class StockAnalyzerService:
             
             # 输出扫描完成信息
             yield json.dumps({
-                "status": "completed",
+                "scan_completed": True,
                 "total_scanned": len(results),
                 "total_matched": len(filtered_results)
             })