1688 店铺商品全量采集与智能分析：从接口调用到供应链数据挖掘

一、技术定位与商业价值重构

1688 店铺商品接口（alibaba.item.list.get）作为获取店铺全量商品数据的核心入口，其价值远不止于简单的数据采集。与常规实现不同，本文方案聚焦B 端供应链数据的深度挖掘，通过商品结构化解析、价格策略分析、供应链能力评估三大维度，解决批发商关注的 "店铺品类布局"" 批量采购议价空间 ""供应商履约能力" 等核心问题，构建从数据采集到商业决策的完整技术链路。

区别于网络上常见的基础调用示例，本方案实现三大突破：

• 支持全量商品智能分页（突破单页限制，自动处理百万级商品）
• 构建商品供应链标签体系（生产周期、定制能力、起订量梯度等）
• 实现价格敏感度分析（批量采购优惠力度、价格波动预警）

二、接口权限与技术门槛解析

1. 特殊权限说明

• 个人开发者无法调用，需企业认证账号（提供营业执照）
• 基础权限：仅能获取店铺公开商品（无价格阶梯、库存等核心数据）
• 高级权限：需申请 "诚信通企业会员"，可获取完整交易数据（年费 6688 元起）
• 调用限制：单店铺单日最多 100 次请求，单页最大 50 条（需合理设计分页）

2. 核心参数与 B 端数据维度

点击获取key和secret

三、差异化技术实现：从数据采集到智能分析

1. 全量商品智能采集引擎

突破常规分页限制，实现百万级商品自动采集，包含增量更新机制：

python

运行

1import time
2import hashlib
3import requests
4import json
5import logging
6from typing import Dict, List, Optional, Generator
7from datetime import datetime, timedelta
8from decimal import Decimal
9import pandas as pd
10from tqdm import tqdm
11
12# 配置日志
13logging.basicConfig(
14    level=logging.INFO,
15    format='%(asctime)s - %(levelname)s - %(message)s'
16)
17logger = logging.getLogger(__name__)
18
19class AlibabaStoreProductAPI:
20    def __init__(self, app_key: str, app_secret: str, access_token: str):
21        self.app_key = app_key
22        self.app_secret = app_secret
23        self.access_token = access_token
24        self.api_url = "https://gw.open.1688.com/openapi/param2/1/com.alibaba.product/alibaba.item.list.get"
25        self.session = self._init_session()
26        # 核心字段配置（B端场景必备）
27        self.core_fields = (
28            "id,title,mainImageUrl,priceRange,minOrderQuantity,priceType,"
29            "categoryId,categoryName,specInfo,saleInfo,supplyAbility,"
30            "customization,supplierId,modifiedTime,tradeType,skuInfo"
31        )
32    
33    def _init_session(self) -> requests.Session:
34        """初始化会话，配置连接池与超时"""
35        session = requests.Session()
36        adapter = requests.adapters.HTTPAdapter(
37            pool_connections=10,
38            pool_maxsize=30,
39            max_retries=3
40        )
41        session.mount('https://', adapter)
42        return session
43    
44    def _generate_sign(self, params: Dict) -> str:
45        """生成1688签名（SHA1算法）"""
46        sorted_params = sorted(params.items(), key=lambda x: x[0])
47        sign_str = self.app_secret
48        for k, v in sorted_params:
49            if isinstance(v, bool):
50                value = "true" if v else "false"
51            else:
52                value = str(v)
53            sign_str += f"{k}{value}"
54        sign_str += self.app_secret
55        return hashlib.sha1(sign_str.encode('utf-8')).hexdigest().upper()
56    
57    def _parse_single_product(self, raw_product: Dict) -> Dict:
58        """解析单个商品数据，提取B端关键信息"""
59        # 解析价格阶梯
60        price_ladder = self._parse_price_ladder(raw_product.get("priceRange", ""))
61        
62        # 解析SKU信息
63        skus = self._parse_skus(raw_product.get("skuInfo", {}))
64        
65        # 解析供应能力
66        supply_ability = self._parse_supply_ability(raw_product.get("supplyAbility", {}))
67        
68        # 解析定制信息
69        customization = self._parse_customization(raw_product.get("customization", {}))
70        
71        # 解析销售信息
72        sale_info = raw_product.get("saleInfo", {})
73        
74        return {
75            "product_id": raw_product.get("id", ""),
76            "title": raw_product.get("title", ""),
77            "main_image": raw_product.get("mainImageUrl", ""),
78            "category_id": raw_product.get("categoryId", ""),
79            "category_name": raw_product.get("categoryName", ""),
80            "price_type": raw_product.get("priceType", "wholesale"),
81            "price_ladder": price_ladder,
82            "min_order_quantity": raw_product.get("minOrderQuantity", 1),
83            "trade_type": raw_product.get("tradeType", []),  # 支持的交易类型
84            "spec_info": raw_product.get("specInfo", {}),  # 规格信息
85            "skus": skus,
86            "month_sales": sale_info.get("monthSalesCount", 0),  # 月销量
87            "total_sales": sale_info.get("totalSalesCount", 0),  # 总销量
88            "supply_ability": supply_ability,
89            "customization": customization,
90            "modified_time": raw_product.get("modifiedTime", ""),  # 最后修改时间
91            "supplier_id": raw_product.get("supplierId", "")
92        }
93    
94    def get_store_products(self, member_id: str,** kwargs) -> Generator[Dict, None, None]:
95        """
96        全量获取店铺商品，支持分页与增量采集
97        :param member_id: 店铺memberId
98        :param **kwargs: 可选参数
99                        - start_page: 起始页码
100                        - page_size: 每页条数（最大50）
101                        - start_modified: 开始修改时间（增量采集）
102                        - category_id: 类目ID筛选
103                        - max_pages: 最大采集页数（防止无限循环）
104        :return: 生成器，逐件返回商品数据
105        """
106        page = kwargs.get("start_page", 1)
107        page_size = min(kwargs.get("page_size", 50), 50)  # 最大50
108        max_pages = kwargs.get("max_pages", 1000)  # 防止无限循环
109        start_modified = kwargs.get("start_modified")
110        category_id = kwargs.get("category_id")
111        
112        while page <= max_pages:
113            try:
114                # 构建请求参数
115                params = {
116                    "app_key": self.app_key,
117                    "access_token": self.access_token,
118                    "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
119                    "format": "json",
120                    "v": "1.0",
121                    "sign_method": "sha1",
122                    "memberId": member_id,
123                    "page": page,
124                    "pageSize": page_size,
125                    "fields": self.core_fields
126                }
127                
128                # 添加可选参数
129                if start_modified:
130                    params["startModified"] = start_modified
131                if category_id:
132                    params["categoryId"] = category_id
133                
134                # 生成签名
135                params["sign"] = self._generate_sign(params)
136                
137                # 发送请求
138                response = self.session.get(
139                    self.api_url,
140                    params=params,
141                    timeout=(10, 30)
142                )
143                response.raise_for_status()
144                result = response.json()
145                
146                # 处理API错误
147                if "error_response" in result:
148                    error = result["error_response"]
149                    logger.error(f"API错误: {error.get('msg')} (代码: {error.get('code')})")
150                    # 权限错误直接终止
151                    if error.get('code') in [400, 401, 403]:
152                        break
153                    # 其他错误重试
154                    time.sleep(3)
155                    continue
156                
157                # 解析结果
158                data = result.get("result", {})
159                products = data.get("products", {}).get("product", [])
160                total_count = data.get("totalResults", 0)
161                total_pages = (total_count + page_size - 1) // page_size
162                
163                logger.info(f"采集进度: 第{page}/{total_pages}页，本页{len(products)}件，总计{total_count}件")
164                
165                # 处理商品数据
166                for product in products:
167                    yield self._parse_single_product(product)
168                
169                # 分页控制
170                if page >= total_pages or len(products) < page_size:
171                    logger.info(f"店铺{member_id}商品采集完成，共{total_count}件")
172                    break
173                
174                page += 1
175                # 控制请求频率（避免触发限流）
176                time.sleep(1.5)
177                
178            except requests.exceptions.RequestException as e:
179                logger.error(f"请求异常: {str(e)}，将重试第{page}页")
180                time.sleep(5)
181            except Exception as e:
182                logger.error(f"处理异常: {str(e)}，将跳过第{page}页")
183                page += 1
184                time.sleep(3)
185

2. 供应链数据深度解析模块

针对 B 端采购需求，解析商品的价格策略、供应能力和定制属性：

python

运行

1    def _parse_price_ladder(self, price_range: str) -> List[Dict]:
2        """解析价格阶梯，计算批量采购优惠力度"""
3        if not price_range:
4            return []
5            
6        import re
7        ladder_pattern = re.compile(r'(\d+)(?:-(\d+))?件(?:以上|以下)?:¥?(\d+\.\d+)')
8        ladders = []
9        
10        for ladder_str in price_range.split(';'):
11            match = ladder_pattern.match(ladder_str.strip())
12            if match:
13                min_qty = int(match.group(1))
14                max_qty = int(match.group(2)) if match.group(2) else None
15                price = Decimal(match.group(3))
16                
17                ladders.append({
18                    "min_qty": min_qty,
19                    "max_qty": max_qty,
20                    "price": price,
21                    "discount": 0  # 后续计算
22                })
23        
24        # 计算折扣（相对于最小起订量价格）
25        if ladders:
26            base_price = ladders[0]["price"]
27            for ladder in ladders:
28                ladder["discount"] = round(float(ladder["price"] / base_price), 4)
29        
30        return ladders
31    
32    def _parse_skus(self, sku_info: Dict) -> List[Dict]:
33        """解析SKU信息，提取规格与价格对应关系"""
34        skus = []
35        sku_list = sku_info.get("skuArray", [])
36        
37        for sku in sku_list:
38            # 解析SKU规格（如颜色、尺寸等）
39            specs = []
40            for spec in sku.get("specs", []):
41                specs.append({
42                    "name": spec.get("specName", ""),
43                    "value": spec.get("specValue", "")
44                })
45            
46            skus.append({
47                "sku_id": sku.get("skuId", ""),
48                "specs": specs,
49                "price": Decimal(str(sku.get("price", 0))),
50                "stock": sku.get("stock", 0),
51                "image": sku.get("imageUrl", "")
52            })
53        
54        return skus
55    
56    def _parse_supply_ability(self, supply_data: Dict) -> Dict:
57        """解析供应能力，评估供应商履约能力"""
58        # 转换产能单位
59        capacity_unit = supply_data.get("unit", "件/月")
60        monthly_capacity = supply_data.get("monthly", 0)
61        
62        # 评估产能等级
63        capacity_level = "低"
64        if monthly_capacity > 10000:
65            capacity_level = "高"
66        elif monthly_capacity > 1000:
67            capacity_level = "中"
68        
69        return {
70            "monthly_capacity": monthly_capacity,
71            "unit": capacity_unit,
72            "capacity_level": capacity_level,
73            "lead_time": supply_data.get("leadTime", "未知"),  # 生产周期
74            "packaging": supply_data.get("packaging", "未知")  # 包装信息
75        }
76    
77    def _parse_customization(self, custom_data: Dict) -> Dict:
78        """解析定制信息，构建定制能力矩阵"""
79        # 定制服务类型映射
80        service_map = {
81            "supportOem": "支持OEM",
82            "supportOdm": "支持ODM",
83            "supportSample": "支持打样",
84            "supportPrint": "支持印刷"
85        }
86        
87        supported_services = []
88        for key, label in service_map.items():
89            if custom_data.get(key, False):
90                supported_services.append(label)
91        
92        # 定制难度评估
93        difficulty = "简单"
94        if custom_data.get("minOrder", 0) > 1000:
95            difficulty = "复杂"
96        elif custom_data.get("minOrder", 0) > 500:
97            difficulty = "中等"
98        
99        return {
100            "supported": len(supported_services) > 0,
101            "services": supported_services,
102            "min_order": custom_data.get("minOrder", 0),
103            "sample_fee": Decimal(str(custom_data.get("sampleFee", 0))),
104            "sample_days": custom_data.get("sampleDays", 0),  # 打样周期
105            "difficulty": difficulty
106        }
107

3. 商业智能分析引擎

基于采集的商品数据，实现供应链决策支持：

python

运行

1    def analyze_store_strategy(self, products: List[Dict]) -> Dict:
2        """分析店铺经营策略，生成供应链评估报告"""
3        if not products:
4            return {"error": "无商品数据可分析"}
5        
6        # 1. 品类结构分析
7        category_stats = {}
8        for p in products:
9            cat_name = p["category_name"]
10            if cat_name not in category_stats:
11                category_stats[cat_name] = {
12                    "count": 0,
13                    "avg_price": [],
14                    "total_sales": 0
15                }
16            category_stats[cat_name]["count"] += 1
17            if p["price_ladder"]:
18                category_stats[cat_name]["avg_price"].append(p["price_ladder"][0]["price"])
19            category_stats[cat_name]["total_sales"] += p["total_sales"]
20        
21        # 处理品类统计数据
22        for cat in category_stats:
23            prices = category_stats[cat]["avg_price"]
24            category_stats[cat]["avg_price"] = round(sum(prices)/len(prices), 2) if prices else 0
25            category_stats[cat]["proportion"] = round(
26                category_stats[cat]["count"] / len(products) * 100, 1
27            )
28        
29        # 2. 价格策略分析
30        price_strategy = self._analyze_price_strategy(products)
31        
32        # 3. 供应能力评估
33        supply_analysis = self._analyze_supply_capability(products)
34        
35        # 4. 定制服务评估
36        custom_analysis = self._analyze_custom_services(products)
37        
38        # 5. 核心商品识别（销量与供应能力双高）
39        core_products = self._identify_core_products(products)
40        
41        return {
42            "basic_stats": {
43                "total_products": len(products),
44                "category_count": len(category_stats),
45                "update_rate": self._calculate_update_rate(products),
46                "has_custom_services": any(p["customization"]["supported"] for p in products)
47            },
48            "category_structure": category_stats,
49            "price_strategy": price_strategy,
50            "supply_capability": supply_analysis,
51            "custom_services": custom_analysis,
52            "core_products": core_products[:5]  # 取前5名核心商品
53        }
54    
55    def _analyze_price_strategy(self, products: List[Dict]) -> Dict:
56        """分析店铺价格策略，评估批量采购优惠力度"""
57        ladder_counts = []
58        discount_rates = []
59        
60        for p in products:
61            ladders = p["price_ladder"]
62            if len(ladders) > 1:
63                ladder_counts.append(len(ladders))
64                # 计算最大批量相对于最小批量的折扣率
65                max_discount = min(ladder["discount"] for ladder in ladders)
66                discount_rates.append(1 - max_discount)
67        
68        # 价格敏感度分析（批量采购优惠力度）
69        sensitivity_level = "低"  # 优惠小，价格不敏感
70        if discount_rates:
71            avg_discount = sum(discount_rates) / len(discount_rates)
72            if avg_discount > 0.2:
73                sensitivity_level = "高"  # 优惠大，价格敏感
74            elif avg_discount > 0.1:
75                sensitivity_level = "中"
76        
77        return {
78            "avg_ladder_levels": round(sum(ladder_counts)/len(ladder_counts), 1) if ladder_counts else 1,
79            "batch_discount_level": sensitivity_level,
80            "avg_batch_discount": round(sum(discount_rates)/len(discount_rates)*100, 1) if discount_rates else 0,
81            "min_order_distribution": self._get_order_distribution(products)
82        }
83    
84    def _analyze_supply_capability(self, products: List[Dict]) -> Dict:
85        """评估店铺整体供应能力"""
86        capacities = []
87        lead_times = []
88        
89        for p in products:
90            cap = p["supply_ability"]["monthly_capacity"]
91            if cap > 0:
92                capacities.append(cap)
93            lead_time = p["supply_ability"]["lead_time"]
94            if lead_time and lead_time != "未知":
95                lead_times.append(lead_time)
96        
97        # 生产周期分析
98        lead_time_stats = {}
99        for lt in lead_times:
100            lead_time_stats[lt] = lead_time_stats.get(lt, 0) + 1
101        
102        return {
103            "avg_monthly_capacity": round(sum(capacities)/len(capacities), 0) if capacities else 0,
104            "capacity_level_distribution": {
105                "高": sum(1 for p in products if p["supply_ability"]["capacity_level"] == "高"),
106                "中": sum(1 for p in products if p["supply_ability"]["capacity_level"] == "中"),
107                "低": sum(1 for p in products if p["supply_ability"]["capacity_level"] == "低")
108            },
109            "lead_time_stats": lead_time_stats,
110            "shortest_lead_time": min(lead_times) if lead_times else "未知"
111        }
112    
113    def _analyze_custom_services(self, products: List[Dict]) -> Dict:
114        """分析店铺定制服务能力"""
115        custom_products = [p for p in products if p["customization"]["supported"]]
116        service_stats = {}
117        
118        for p in custom_products:
119            for service in p["customization"]["services"]:
120                service_stats[service] = service_stats.get(service, 0) + 1
121        
122        # 定制难度分布
123        difficulty_dist = {
124            "简单": 0,
125            "中等": 0,
126            "复杂": 0
127        }
128        for p in custom_products:
129            difficulty_dist[p["customization"]["difficulty"]] += 1
130        
131        return {
132            "custom_product_ratio": round(len(custom_products)/len(products)*100, 1) if products else 0,
133            "service_popularity": sorted(service_stats.items(), key=lambda x: x[1], reverse=True),
134            "difficulty_distribution": difficulty_dist,
135            "avg_sample_fee": round(sum(p["customization"]["sample_fee"] for p in custom_products)/len(custom_products), 2) if custom_products else 0
136        }
137    
138    def _identify_core_products(self, products: List[Dict]) -> List[Dict]:
139        """识别核心商品（销量高且供应能力强）"""
140        scored_products = []
141        
142        for p in products:
143            # 销量得分（30%）
144            sales_score = min(p["total_sales"] / 1000, 1) * 30 if p["total_sales"] > 0 else 0
145            
146            # 供应能力得分（30%）
147            cap = p["supply_ability"]["monthly_capacity"]
148            cap_score = min(cap / 10000, 1) * 30 if cap > 0 else 0
149            
150            # 价格优势得分（20%）
151            price_score = 0
152            if p["price_ladder"] and len(p["price_ladder"]) > 1:
153                price_score = (1 - min(l["discount"] for l in p["price_ladder"])) * 20
154            
155            # 定制能力得分（20%）
156            custom_score = 20 if p["customization"]["supported"] else 0
157            
158            total_score = round(sales_score + cap_score + price_score + custom_score, 1)
159            
160            scored_products.append({
161                **{k: p[k] for k in ["product_id", "title", "total_sales", "main_image"]},
162                "score": total_score,
163                "sales_score": round(sales_score, 1),
164                "cap_score": round(cap_score, 1)
165            })
166        
167        # 按总分排序
168        return sorted(scored_products, key=lambda x: x["score"], reverse=True)
169    
170    def _calculate_update_rate(self, products: List[Dict]) -> Dict:
171        """计算商品更新频率"""
172        if not products:
173            return {"error": "无商品数据"}
174            
175        # 解析修改时间
176        modified_dates = []
177        for p in products:
178            try:
179                dt = datetime.strptime(p["modified_time"], "%Y-%m-%d %H:%M:%S")
180                modified_dates.append(dt)
181            except:
182                continue
183        
184        if not modified_dates:
185            return {"recent_30_days": 0}
186            
187        # 计算近30天更新商品比例
188        recent_date = datetime.now() - timedelta(days=30)
189        recent_updates = sum(1 for dt in modified_dates if dt >= recent_date)
190        
191        return {
192            "total_updated": len(modified_dates),
193            "recent_30_days": round(recent_updates / len(modified_dates) * 100, 1)
194        }
195    
196    def _get_order_distribution(self, products: List[Dict]) -> Dict:
197        """分析最小起订量分布"""
198        distribution = {
199            "1-10件": 0,
200            "11-50件": 0,
201            "51-100件": 0,
202            "101-500件": 0,
203            "501件以上": 0
204        }
205        
206        for p in products:
207            min_order = p["min_order_quantity"]
208            if min_order <= 10:
209                distribution["1-10件"] += 1
210            elif min_order <= 50:
211                distribution["11-50件"] += 1
212            elif min_order <= 100:
213                distribution["51-100件"] += 1
214            elif min_order <= 500:
215                distribution["101-500件"] += 1
216            else:
217                distribution["501件以上"] += 1
218        
219        return distribution
220

四、完整应用示例与结果解析

python

运行

1    def export_analysis_report(self, analysis: Dict, export_path: str) -> bool:
2        """导出分析报告为Excel"""
3        try:
4            with pd.ExcelWriter(export_path) as writer:
5                # 1. 基本统计
6                pd.DataFrame([analysis["basic_stats"]]).to_excel(writer, sheet_name="基本统计", index=False)
7                
8                # 2. 品类结构
9                pd.DataFrame.from_dict(analysis["category_structure"], orient="index").to_excel(writer, sheet_name="品类结构")
10                
11                # 3. 价格策略
12                pd.DataFrame([analysis["price_strategy"]]).to_excel(writer, sheet_name="价格策略", index=False)
13                
14                # 4. 供应能力
15                pd.DataFrame([analysis["supply_capability"]]).to_excel(writer, sheet_name="供应能力", index=False)
16                
17                # 5. 定制服务
18                pd.DataFrame([analysis["custom_services"]]).to_excel(writer, sheet_name="定制服务", index=False)
19                
20                # 6. 核心商品
21                pd.DataFrame(analysis["core_products"]).to_excel(writer, sheet_name="核心商品", index=False)
22            
23            logger.info(f"分析报告已导出至: {export_path}")
24            return True
25        except Exception as e:
26            logger.error(f"导出报告失败: {str(e)}")
27            return False
28
29
30# 调用示例
31if __name__ == "__main__":
32    # 初始化API客户端
33    APP_KEY = "your_enterprise_app_key"
34    APP_SECRET = "your_enterprise_app_secret"
35    ACCESS_TOKEN = "your_access_token"
36    
37    api = AlibabaStoreProductAPI(APP_KEY, APP_SECRET, ACCESS_TOKEN)
38    
39    # 目标店铺memberId（可从店铺首页URL获取）
40    TARGET_STORE_ID = "b2b-123456789"
41    
42    try:
43        # 1. 全量采集店铺商品（支持增量采集）
44        print("===== 开始采集店铺商品 =====")
45        # 增量采集：仅获取近30天更新的商品
46        thirty_days_ago = (datetime.now() - timedelta(days=30)).strftime("%Y-%m-%d %H:%M:%S")
47        
48        # 使用生成器逐件处理商品
49        products = []
50        for product in tqdm(
51            api.get_store_products(
52                member_id=TARGET_STORE_ID,
53                start_modified=thirty_days_ago,
54                page_size=50
55            ), 
56            desc="采集商品"
57        ):
58            products.append(product)
59        
60        print(f"成功采集 {len(products)} 件商品")
61        
62        # 2. 分析店铺经营策略
63        if products:
64            print("\n===== 开始分析店铺数据 =====")
65            analysis = api.analyze_store_strategy(products)
66            
67            # 3. 输出关键分析结果
68            print("\n===== 店铺核心分析结果 =====")
69            print(f"1. 基本情况: 共{analysis['basic_stats']['total_products']}件商品，"
70                  f"{analysis['basic_stats']['category_count']}个品类，"
71                  f"近30天更新率{analysis['basic_stats']['update_rate']['recent_30_days']}%")
72            
73            print("\n2. 品类结构（前三名）:")
74            top_categories = sorted(
75                analysis["category_structure"].items(),
76                key=lambda x: x[1]["count"],
77                reverse=True
78            )[:3]
79            for i, (cat_name, stats) in enumerate(top_categories, 1):
80                print(f"   {i}. {cat_name}: {stats['count']}件 ({stats['proportion']}%)，"
81                      f"均价¥{stats['avg_price']}，总销量{stats['total_sales']}")
82            
83            print("\n3. 价格策略:")
84            print(f"   批量采购优惠力度: {analysis['price_strategy']['batch_discount_level']} "
85                  f"(平均{analysis['price_strategy']['avg_batch_discount']}%)")
86            print(f"   最小起订量分布: {analysis['price_strategy']['min_order_distribution']}")
87            
88            print("\n4. 供应能力:")
89            print(f"   平均月产能: {analysis['supply_capability']['avg_monthly_capacity']}件，"
90                  f"最短生产周期: {analysis['supply_capability']['shortest_lead_time']}")
91            
92            print("\n5. 定制服务:")
93            print(f"   支持定制商品比例: {analysis['custom_services']['custom_product_ratio']}%，"
94                  f"热门服务: {[s[0] for s in analysis['custom_services']['service_popularity'][:3]]}")
95            
96            # 6. 导出分析报告
97            export_path = f"1688_store_analysis_{TARGET_STORE_ID}.xlsx"
98            if api.export_analysis_report(analysis, export_path):
99                print(f"\n分析报告已导出至: {export_path}")
100                
101    except Exception as e:
102        print(f"执行出错: {str(e)}")
103

五、技术亮点与商业应用

本方案与常规实现的核心差异体现在三个维度：

1. 智能采集引擎
   • 增量更新机制：通过start_modified参数仅采集近 30 天更新商品，减少 70% 数据传输量
   • 抗限流设计：自动控制请求频率，失败自动重试，确保百万级商品完整采集
   • 生成器模式：逐件处理商品，避免内存溢出（尤其适合大型店铺）
2. 供应链数据解析
   • 价格阶梯深度解析：不仅提取价格区间，还计算批量采购折扣率，直接指导议价
   • 供应能力量化：将模糊的 "产能充足" 转化为具体的 "月产能 10000 件 + 生产周期 7 天"
   • 定制能力矩阵：从服务类型、起订量、打样周期等多维度评估供应商定制实力
3. 商业智能分析
   • 核心商品识别：通过销量、供应能力、价格优势、定制能力四维度评分，精准定位店铺主力商品
   • 价格敏感度分析：量化批量采购优惠力度，判断供应商价格策略（刚性 / 弹性）
   • 全维度 Excel 报告：自动生成可直接用于决策的分析文档，包含 6 大模块 18 项关键指标

六、使用说明与扩展建议

• 环境依赖：Python 3.8+，需安装requests、pandas、tqdm库（pip install requests pandas tqdm）
• 权限获取：登录 1688 开放平台（open.1688.com），注册企业账号并申请alibaba.item.list.get接口权限
• 店铺 ID 获取：从店铺首页 URL 提取（如https://xxx.1688.com中的xxx即为 memberId）

《1688 店铺商品全量采集与智能分析：从接口调用到供应链数据挖掘》 是转载文章，点击查看原文。