为什么wordpress后台写文章不一样黑帽seo技术论坛
文章目录
- 前言
- 项目介绍
- 技术介绍
- 功能介绍
- 核心代码
- 数据库参考
- 系统效果图
前言
文章底部名片，获取项目的完整演示视频，免费解答技术疑问
项目介绍
随着开数字化阅读的普及，豆瓣电子图书推荐系统应运而生，旨在为用户提供个性化的阅读体验。基于Hadoop的强大数据处理能力，该系统能够有效处理海量用户数据和书籍信息，通过复杂的算法模型为用户推荐高质量的内容。管理员功能涵盖用户管理、豆瓣高分管理等，确保了平台的高效运营。用户个人中心则提供修改密码、我的发布等服务，增强了用户体验。整体上，该推荐系统不仅提升了用户的阅读便利性，也促进了知识分享与文化交流。
根据本系统的基本设计思路，本系统在设计方面前台采用了java技术等进行基本的页面设计，后台数据库采用MySQL。本系统的实现为豆瓣电子图书推荐系统的运行打下了基础，为豆瓣电子图书推荐提供良好的条件。
最后我们通过需求分析、测试调整，与豆瓣电子图书管理的实际需求相结合，设计实现了豆瓣电子图书推荐系统。
技术介绍
开发语言：Java
框架：springboot
JDK版本：JDK1.8
服务器：tomcat7
数据库：mysql
数据库工具：Navicat11
开发软件：eclipse/myeclipse/idea
Maven包：Maven
功能介绍
系统的功能设计是整个系统的运行基础，是一个把设计需求替换成以计算机系统的形式表示出来。通过对豆瓣电子图书推荐系统的调查、分析和研究，得出了该系统的总体规划，这是开发设计系统的初步核心。如下图所示：
在这里插入图片描述
图4-1总体规划结构图
核心代码
# # -*- coding: utf-8 -*-# 数据爬取文件import scrapy
import pymysql
import pymssql
from ..items import DoubanItem
import time
from datetime import datetime,timedelta
import datetime as formattime
import re
import random
import platform
import json
import os
import urllib
from urllib.parse import urlparse
import requests
import emoji
import numpy as np
import pandas as pd
from sqlalchemy import create_engine
from selenium.webdriver import ChromeOptions, ActionChains
from scrapy.http import TextResponse
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.support.wait import WebDriverWait
# 豆瓣高分
class DoubanSpider(scrapy.Spider):name = 'doubanSpider'spiderUrl = 'https://read.douban.com/j/kind/'start_urls = spiderUrl.split(";")protocol = ''hostname = ''realtime = Falsedef __init__(self,realtime=False,*args, **kwargs):super().__init__(*args, **kwargs)self.realtime = realtime=='true'def start_requests(self):plat = platform.system().lower()if not self.realtime and (plat == 'linux' or plat == 'windows'):connect = self.db_connect()cursor = connect.cursor()if self.table_exists(cursor, 'l908648a_douban') == 1:cursor.close()connect.close()self.temp_data()returndata_json={"sort": "book_rating","page": 1,"kind": 100,"query": "\n query getFilterWorksList($works_ids: [ID!]) {\n worksList(worksIds: $works_ids) {\n \n id\n isOrigin\n isEssay\n \n title\n cover(useSmall: false)\n url\n isBundle\n coverLabel(preferVip: true)\n \n \n url\n title\n\n \n author {\n name\n url\n }\n origAuthor {\n name\n url\n }\n translator {\n name\n url\n }\n\n \n abstract\n authorHighlight\n editorHighlight\n\n \n isOrigin\n kinds {\n \n name @skip(if: true)\n shortName @include(if: true)\n id\n \n }\n ... on WorksBase @include(if: true) {\n wordCount\n wordCountUnit\n }\n ... on WorksBase @include(if: false) {\n inLibraryCount\n }\n ... on WorksBase @include(if: false) {\n \n isEssay\n \n ... on EssayWorks {\n favorCount\n }\n \n \n \n averageRating\n ratingCount\n url\n isColumn\n isFinished\n \n \n \n }\n ... on EbookWorks @include(if: true) {\n \n ... on EbookWorks {\n book {\n url\n averageRating\n ratingCount\n }\n }\n \n }\n ... on WorksBase @include(if: false) {\n isColumn\n isEssay\n onSaleTime\n ... on ColumnWorks {\n updateTime\n }\n }\n ... on WorksBase @include(if: true) {\n isColumn\n ... on ColumnWorks {\n isFinished\n }\n }\n ... on EssayWorks {\n essayActivityData {\n \n title\n uri\n tag {\n name\n color\n background\n icon2x\n icon3x\n iconSize {\n height\n }\n iconPosition {\n x y\n }\n }\n \n }\n }\n highlightTags {\n name\n }\n ... on WorksBase @include(if: false) {\n fanfiction {\n tags {\n id\n name\n url\n }\n }\n }\n \n \n ... on WorksBase {\n copyrightInfo {\n newlyAdapted\n newlyPublished\n adaptedName\n publishedName\n }\n }\n\n isInLibrary\n ... on WorksBase @include(if: false) {\n \n fixedPrice\n salesPrice\n isRebate\n \n }\n ... on EbookWorks {\n \n fixedPrice\n salesPrice\n isRebate\n \n }\n ... on WorksBase @include(if: true) {\n ... on EbookWorks {\n id\n isPurchased\n isInWishlist\n }\n }\n ... on WorksBase @include(if: false) {\n fanfiction {\n fandoms {\n title\n url\n }\n }\n }\n ... on WorksBase @include(if: false) {\n fanfiction {\n kudoCount\n }\n }\n \n }\n }\n ","variables": {}}pageNum = 1 + 1for page in range(1, pageNum):data_json["page"] = pageraw = json.dumps(data_json)yield scrapy.FormRequest(url=self.spiderUrl,body=raw,method='POST',callback=self.parse,headers={'Content-Type': 'application/json'},dont_filter=True)# 列表解析def parse(self, response):_url = urlparse(self.spiderUrl)self.protocol = _url.schemeself.hostname = _url.netlocplat = platform.system().lower()if not self.realtime and (plat == 'linux' or plat == 'windows'):connect = self.db_connect()cursor = connect.cursor()if self.table_exists(cursor, 'l908648a_douban') == 1:cursor.close()connect.close()self.temp_data()returnprint("response:",response.text)data = json.loads(response.text)try:list = data["list"]except:passfor item in list:fields = DoubanItem()fields["bookname"] = item["title"]fields["cover"] = item["cover"]fields["laiyuan"] = "https://read.douban.com"+item["url"]fields["wordcount"] = int(item["wordCount"])fields["salesprice"] = float(item["salesPrice"])yield scrapy.Request(url="https://read.douban.com/j"+item["url"],meta={"fields":fields},callback=self.detail_parse,dont_filter=True)# 详情解析def detail_parse(self, response):print("detail_parse:",response.text)fields = response.meta['fields']data = json.loads(response.text)try:fields["author"] = data["author"]except:fields["author"] = data["original_author"]try:fields["chuban"] = data["publisher"]except:passtry:fields["tags"] = ",".join(i["tag"] for i in data["tags"])except:passtry:fields["mulu"] = ";".join(i["title"] for i in data["table_of_contents"])except:passtry:fields["rating"] = data["book_average_rating"]except:passreturn fields# 数据清洗def pandas_filter(self):engine = create_engine('mysql+pymysql://root:123456@localhost/spiderl908648a?charset=UTF8MB4')df = pd.read_sql('select * from douban limit 50', con = engine)# 重复数据过滤df.duplicated()df.drop_duplicates()#空数据过滤df.isnull()df.dropna()# 填充空数据df.fillna(value = '暂无')# 异常值过滤# 滤出 大于800 和 小于 100 的a = np.random.randint(0, 1000, size = 200)cond = (a<=800) & (a>=100)a[cond]# 过滤正态分布的异常值b = np.random.randn(100000)# 3σ过滤异常值，σ即是标准差cond = np.abs(b) > 3 * 1b[cond]# 正态分布数据df2 = pd.DataFrame(data = np.random.randn(10000,3))# 3σ过滤异常值，σ即是标准差cond = (df2 > 3*df2.std()).any(axis = 1)# 不满⾜条件的⾏索引index = df2[cond].index# 根据⾏索引，进⾏数据删除df2.drop(labels=index,axis = 0)# 去除多余html标签def remove_html(self, html):if html == None:return ''pattern = re.compile(r'<[^>]+>', re.S)return pattern.sub('', html).strip()# 数据库连接def db_connect(self):type = self.settings.get('TYPE', 'mysql')host = self.settings.get('HOST', 'localhost')port = int(self.settings.get('PORT', 3306))user = self.settings.get('USER', 'root')password = self.settings.get('PASSWORD', '123456')try:database = self.databaseNameexcept:database = self.settings.get('DATABASE', '')if type == 'mysql':connect = pymysql.connect(host=host, port=port, db=database, user=user, passwd=password, charset='utf8')else:connect = pymssql.connect(host=host, user=user, password=password, database=database)return connect# 断表是否存在def table_exists(self, cursor, table_name):cursor.execute("show tables;")tables = [cursor.fetchall()]table_list = re.findall('(\'.*?\')',str(tables))table_list = [re.sub("'",'',each) for each in table_list]if table_name in table_list:return 1else:return 0# 数据缓存源def temp_data(self):connect = self.db_connect()cursor = connect.cursor()sql = '''insert into `douban`(id,bookname,author,cover,laiyuan,wordcount,salesprice,chuban,tags,mulu,rating)selectid,bookname,author,cover,laiyuan,wordcount,salesprice,chuban,tags,mulu,ratingfrom `l908648a_douban`where(not exists (selectid,bookname,author,cover,laiyuan,wordcount,salesprice,chuban,tags,mulu,ratingfrom `douban` where`douban`.id=`l908648a_douban`.id))'''cursor.execute(sql)connect.commit()connect.close()数据库参考
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CREATE TABLE `douban` (`id` bigint(20) NOT NULL AUTO_INCREMENT COMMENT '主键',`addtime` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',`bookname` varchar(200) DEFAULT NULL COMMENT '书名',`author` varchar(200) DEFAULT NULL COMMENT '作者',`cover` longtext COMMENT '封面',`laiyuan` varchar(200) DEFAULT NULL COMMENT '来源',`wordcount` int(11) DEFAULT NULL COMMENT '字数',`salesprice` double DEFAULT NULL COMMENT '价格',`chuban` varchar(200) DEFAULT NULL COMMENT '出版社',`tags` varchar(200) DEFAULT NULL COMMENT '标签',`mulu` longtext COMMENT '章节目录',`rating` double DEFAULT NULL COMMENT '评分',`thumbsupnum` int(11) DEFAULT '0' COMMENT '赞',`crazilynum` int(11) DEFAULT '0' COMMENT '踩',`clicktime` datetime DEFAULT NULL COMMENT '最近点击时间',`clicknum` int(11) DEFAULT '0' COMMENT '点击次数',`discussnum` int(11) DEFAULT '0' COMMENT '评论数',`storeupnum` int(11) DEFAULT '0' COMMENT '收藏数',PRIMARY KEY (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=29 DEFAULT CHARSET=utf8 COMMENT='豆瓣高分';
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CREATE TABLE `l908648a_douban` (`id` bigint(20) NOT NULL DEFAULT '0' COMMENT '主键',`addtime` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',`bookname` varchar(200) CHARACTER SET utf8 DEFAULT NULL COMMENT '书名',`author` varchar(200) CHARACTER SET utf8 DEFAULT NULL COMMENT '作者',`cover` longtext CHARACTER SET utf8 COMMENT '封面',`laiyuan` longtext CHARACTER SET utf8 COMMENT '来源',`wordcount` int(11) DEFAULT NULL COMMENT '字数',`salesprice` double DEFAULT NULL COMMENT '价格',`chuban` varchar(200) CHARACTER SET utf8 DEFAULT NULL COMMENT '出版社',`tags` varchar(200) CHARACTER SET utf8 DEFAULT NULL COMMENT '标签',`mulu` longtext CHARACTER SET utf8 COMMENT '章节目录',`rating` double DEFAULT NULL COMMENT '评分'
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;
/*!40101 SET character_set_client = @saved_cs_client */;系统效果图
