注：爬取后的信息将以json格式存储，并将文件命名为“recruit.json”可用Notepad++打开。

代码实现：

items.py

# -*- coding: utf-8 -*-

import scrapy


class TxhrItem(scrapy.Item):
    #职位名称
    positionName = scrapy.Field()
    #职位类别
    positionType = scrapy.Field()
    #需求人数
    needNum = scrapy.Field()
    #工作地点
    workingSpace = scrapy.Field()
    #发布时间
    publishTime = scrapy.Field()

爬虫文件（spiders/txHRSpider.py）

# -*- coding: utf-8 -*-
import scrapy
from txHR.items import TxhrItem


class TxhrspiderSpider(scrapy.Spider):
    name = 'txHR'
    allowed_domains = ['tencent.com']
    initialURL = 'https://hr.tencent.com/position.php?@start=&start='
    bias = 0
    url = initialURL + str(bias)
    start_urls = [url]

    def parse(self, response):
        # even=偶,odd=奇
        for each in response.xpath("//tr[@class='even'] | //tr[@class='odd']"):
            #创建模型对象
            item = TxhrItem()
            item['positionName'] = each.xpath("td[1]/a/text()").extract()[0]
            test_null = each.xpath("td[2]/text()").extract()
            #因为网页中有的记录中的“职位类别”为空，如果不加上下面的判断，程序会在中途报错
            if test_null == []:
                item['positionType'] = "Null"
            else:
                item['positionType'] = test_null[0]
            item['needNum'] = each.xpath("td[3]/text()").extract()[0]
            item['workingSpace'] = each.xpath("td[4]/text()").extract()[0]
            item['publishTime'] = each.xpath("td[5]/text()").extract()[0]
            yield item
        self.bias += 10
        #抓取前1000条社会招聘信息
        if self.bias < 1000:
            url = self.initialURL+str(self.bias)
            yield scrapy.Request(url, callback=self.parse)

pipelines.py

# -*- coding: utf-8 -*-

import json


class TxhrPipeline(object):
    def __init__(self):
        self.output = open("recruit.json", 'w')

    def process_item(self, item, spider):
        # 将爬取的信息先转换为字典，再转换为json格式的键值对
        jsonText = json.dumps(obj=dict(item), ensure_ascii=False) + '\n'
        self.output.write(jsonText)
        return item

    def close_spider(self):
        self.output.close()

settings.py

BOT_NAME = 'txHR'

SPIDER_MODULES = ['txHR.spiders']
NEWSPIDER_MODULE = 'txHR.spiders'

ROBOTSTXT_OBEY = True

DEFAULT_REQUEST_HEADERS = {
    'user-agent':'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/66.0.3359.139 Safari/537.36',
    'Accept': 'image/webp,image/apng,image/*,*/*;q=0.8',
    'Accept-Language': 'zh-CN,zh;q=0.9',
}

ITEM_PIPELINES = {
    'txHR.pipelines.TxhrPipeline': 300,
}