本项目纯学习使用。

1 scrapy 代码

爬取逻辑非常简单，根据url来处理翻页，然后获取到详情页面的链接，再去爬取详情页面的内容即可，最终数据落地到excel中。
经测试，总计获取 11299条中医药材数据。

import pandas as pd
import scrapy


class ZhongyaoSpider(scrapy.Spider):
    name = "zhongyao"
    start_urls = [f"https://www.zysj.com.cn/zhongyaocai/index__{i}.html" for i in range(1, 27)]

    def __init__(self, *args, **kwargs):
        self.data = []

    def parse(self, response):
        for li in response.css('div#list-content ul li'):
            a_tag = li.css('a')
            title = a_tag.css('::attr(title)').get()
            href = a_tag.css('::attr(href)').get()
            if title and href:
                # 构建完整的详情页 URL
                detail_url = response.urljoin(href)
                yield scrapy.Request(detail_url, callback=self.parse_detail, meta={'title': title})

    # 解析逻辑
    def parse_detail(self, response):
        title = response.meta['title']
        pinyin = response.css('div.item.pinyin_name_phonetic div.item-content::text').get(default='').strip()
        alias = response.css('div.item.alias div.item-content p::text').get(default='').strip()
        english_name = response.css('div.item.english_name div.item-content::text').get(default='').strip()
        source = response.css('div.item.alias div.item-content p::text').get(default='').strip()
        # 性味
        flavor = response.css('div.item.flavor div.item-content p::text').get(default='').strip()
        functional_indications = response.css('div.item.flavor div.item-content p::text').get(default='').strip()
        usage = response.css('div.item.usage div.item-content p::text').get(default='').strip()
        excerpt = response.css('div.item.excerpt div.item-content::text').get(default='').strip()
        #
        habitat = response.css('div.item.habitat div.item-content p::text').get(default='').strip()
        # 出处
        provenance = response.css('div.item.provenance div.item-content p::text').get(default='').strip()
        # 性状
        shape_properties = response.css('div.item.shape_properties div.item-content p::text').get(default='').strip()
        # 归经
        attribution = response.css('div.item.attribution div.item-content p::text').get(default='').strip()
        #  原形态
        prototype = response.css('div.item.prototype div.item-content p::text').get(default='').strip()
        # 名家论述
        discuss = response.css('div.item.discuss div.item-content p::text').get(default='').strip()
        # 化学成分
        chemical_composition = response.css('div.item.chemical_composition div.item-content p::text').get(default='').strip()

        item = {
            'title': title,
            'pinyin': pinyin,
            'alias': alias,
            'source': source,
            'english_name': english_name,
            'habitat': habitat,
            'flavor': flavor,
            'functional_indications': functional_indications,
            'usage': usage,
            'excerpt': excerpt,
            'provenance': provenance,
            'shape_properties': shape_properties,
            'attribution':  attribution,
            'prototype': prototype,
            'discuss': discuss,
            'chemical_composition': chemical_composition,
        }

        self.data.append(item)
        yield item

    def closed(self, reason):
        # 当爬虫关闭时，保存数据到 Excel 文件
        df = pd.DataFrame(self.data)
        df.to_excel('zhongyao_data.xlsx', index=False)

2 爬取截图

3 爬取数据截图