计算机毕设Python项目：基于爬虫技术的网络小说数据分析系统

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💖🔥作者主页：计算机毕设木哥🔥 💖

一、项目介绍

基于爬虫技术的网络小说数据分析系统是一个专门针对网络文学领域进行数据收集与热度分析的综合性平台。该系统采用Python作为核心开发语言，运用成熟的爬虫技术从各大网络小说平台自动抓取小说基础信息、阅读量、评论数据等关键指标，通过MySQL数据库进行结构化存储管理。系统基于Django后端框架构建稳定的API接口，前端采用Vue.js结合ElementUI组件库打造直观友好的用户交互界面。平台设计了管理员和普通用户两种角色权限体系，管理员可以对用户账户、小说信息库、言情小说分类以及阅读趋势预测模块进行全面管理维护，普通用户则能够便捷地注册登录、浏览小说详细信息、查看言情小说专题内容。系统通过自动化的数据采集与智能化的热度分析算法，为网络文学爱好者提供准确的小说热度排行和趋势预测服务，同时为相关研究人员和从业者提供有价值的数据分析支持。

选题背景
随着移动互联网技术的快速发展和智能终端设备的广泛普及，网络文学产业迎来了前所未有的繁荣发展期。各大网络小说平台如雨后春笋般涌现，作品数量呈指数级增长，读者群体规模不断扩大，形成了庞大的数字阅读生态圈。然而，海量的小说作品也给读者的选择带来了困扰，如何从数以万计的作品中快速找到符合个人喜好的优质内容成为普遍需求。传统的人工推荐方式已无法满足用户对精准推荐的期待，而各平台的热度排行往往存在算法不透明、更新滞后等问题。与此同时，网络文学的商业价值日益凸显，IP改编、版权交易等衍生业务蓬勃发展，出版社、影视公司、游戏厂商等都迫切需要准确把握市场热点和趋势走向。在这样的背景下，构建一个能够客观、及时、准确分析网络小说热度的数据分析系统具有重要的现实意义和应用价值。

选题意义
本课题的研究意义体现在多个层面的实际应用价值上。对于普通读者而言，系统能够提供客观的小说热度排行和趋势分析，帮助他们在信息过载的环境中快速筛选出受欢迎的优质作品，提高阅读选择的效率和满意度，降低试错成本。对于网络小说作者群体来说，通过观察热度变化趋势和读者偏好分析，可以更好地把握市场脉搏，调整创作方向，提升作品的市场竞争力和商业价值。从技术实践角度看，该系统整合了网络爬虫、数据分析、Web开发等多项核心技术，为计算机专业学生提供了一个综合性的实战项目，有助于加深对Python编程、数据库设计、前后端分离架构等知识点的理解和应用。对于网络文学行业的从业者和研究人员而言，系统产生的数据分析报告可以为市场决策、内容策划、用户运营等业务环节提供数据支撑，虽然作为毕业设计项目在规模和复杂度上相对有限，但其核心思路和技术方案具有一定的参考价值和实用性。

二、开发环境

• 大数据技术：Hadoop、Spark、Hive
• 开发技术：Python、Django框架、Vue、Echarts
• 软件工具：Pycharm、DataGrip、Anaconda
• 可视化 工具 Echarts

三、视频展示

计算机毕设Python项目：基于爬虫技术的网络小说数据分析系统

四、项目展示

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五、代码展示

from pyspark.sql import SparkSession
import requests
from bs4 import BeautifulSoup
import pandas as pd
from django.http import JsonResponse
from django.db import models
import mysql.connector
import json
import re
from datetime import datetime, timedelta
import numpy as npspark = SparkSession.builder.appName("NovelHeatAnalysis").config("spark.driver.memory", "2g").getOrCreate()def crawl_novel_data(request):target_urls = ['https://www.qidian.com/rank/yuepiao/','https://www.zongheng.com/rank/details.html','https://www.17k.com/rank/popularity/']novel_data_list = []headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36','Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8','Accept-Language': 'zh-CN,zh;q=0.8,zh-TW;q=0.7,zh-HK;q=0.5,en-US;q=0.3,en;q=0.2','Accept-Encoding': 'gzip, deflate','Connection': 'keep-alive','Upgrade-Insecure-Requests': '1'}for url in target_urls:try:response = requests.get(url, headers=headers, timeout=10)response.encoding = 'utf-8'soup = BeautifulSoup(response.text, 'html.parser')novel_items = soup.find_all('div', class_=['book-info', 'book-item', 'rank-item'])for item in novel_items:title_element = item.find(['h3', 'h4', 'a'], class_=['book-title', 'title'])author_element = item.find(['span', 'a'], class_=['author', 'writer'])category_element = item.find(['span', 'em'], class_=['category', 'type'])read_count_element = item.find(['span', 'em'], class_=['read-count', 'click'])if title_element and author_element:title = title_element.get_text().strip()author = author_element.get_text().strip()category = category_element.get_text().strip() if category_element else '未分类'read_count_text = read_count_element.get_text().strip() if read_count_element else '0'read_count = extract_number_from_text(read_count_text)heat_score = calculate_heat_score(read_count, title, category)novel_data = {'title': title,'author': author,'category': category,'read_count': read_count,'heat_score': heat_score,'crawl_time': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),'source_url': url}novel_data_list.append(novel_data)except Exception as e:continuesave_to_database(novel_data_list)return JsonResponse({'status': 'success', 'count': len(novel_data_list), 'message': '数据爬取完成'})def analyze_novel_heat(request):connection = mysql.connector.connect(host='localhost',database='novel_analysis',user='root',password='password')cursor = connection.cursor(dictionary=True)query = """SELECT title, author, category, read_count, heat_score, crawl_time FROM novel_info WHERE crawl_time >= DATE_SUB(NOW(), INTERVAL 7 DAY)ORDER BY heat_score DESC"""cursor.execute(query)recent_data = cursor.fetchall()df = pd.DataFrame(recent_data)spark_df = spark.createDataFrame(df)spark_df.createOrReplaceTempView("novels")category_analysis = spark.sql("""SELECT category, COUNT(*) as novel_count,AVG(read_count) as avg_reads,AVG(heat_score) as avg_heat,MAX(heat_score) as max_heatFROM novels GROUP BY category ORDER BY avg_heat DESC""").toPandas()author_analysis = spark.sql("""SELECT author,COUNT(*) as work_count,SUM(read_count) as total_reads,AVG(heat_score) as avg_heatFROM novels GROUP BY authorHAVING work_count >= 2ORDER BY avg_heat DESCLIMIT 20""").toPandas()trend_analysis = []for i in range(7):date = (datetime.now() - timedelta(days=i)).strftime('%Y-%m-%d')daily_query = f"""SELECT AVG(heat_score) as daily_avg_heat, COUNT(*) as daily_countFROM novel_info WHERE DATE(crawl_time) = '{date}'"""cursor.execute(daily_query)daily_result = cursor.fetchone()if daily_result and daily_result['daily_count'] > 0:trend_analysis.append({'date': date,'avg_heat': float(daily_result['daily_avg_heat']),'novel_count': daily_result['daily_count']})heat_distribution = {'high_heat': len(df[df['heat_score'] >= 80]),'medium_heat': len(df[(df['heat_score'] >= 60) & (df['heat_score'] < 80)]),'low_heat': len(df[df['heat_score'] < 60])}top_novels = df.nlargest(10, 'heat_score')[['title', 'author', 'category', 'heat_score']].to_dict('records')analysis_result = {'category_stats': category_analysis.to_dict('records'),'author_stats': author_analysis.to_dict('records'),'trend_analysis': trend_analysis,'heat_distribution': heat_distribution,'top_novels': top_novels,'total_analyzed': len(df)}cursor.close()connection.close()return JsonResponse({'status': 'success', 'data': analysis_result})def predict_reading_trend(request):connection = mysql.connector.connect(host='localhost',database='novel_analysis',user='root',password='password')cursor = connection.cursor(dictionary=True)historical_query = """SELECT DATE(crawl_time) as date, category,AVG(heat_score) as avg_heat,SUM(read_count) as total_readsFROM novel_info WHERE crawl_time >= DATE_SUB(NOW(), INTERVAL 30 DAY)GROUP BY DATE(crawl_time), categoryORDER BY date DESC, avg_heat DESC"""cursor.execute(historical_query)historical_data = cursor.fetchall()df_historical = pd.DataFrame(historical_data)category_trends = {}for category in df_historical['category'].unique():category_data = df_historical[df_historical['category'] == category].sort_values('date')if len(category_data) >= 5:heat_values = category_data['avg_heat'].valuesread_values = category_data['total_reads'].valuesheat_trend = calculate_trend_direction(heat_values)read_trend = calculate_trend_direction(read_values)prediction_score = (heat_trend + read_trend) / 2next_week_prediction = predict_next_period(heat_values)category_trends[category] = {'current_heat': float(heat_values[-1]) if len(heat_values) > 0 else 0,'trend_direction': 'up' if prediction_score > 0 else 'down' if prediction_score < 0 else 'stable','trend_strength': abs(prediction_score),'predicted_heat': float(next_week_prediction),'confidence': min(0.95, 0.6 + len(category_data) * 0.05)}romance_query = """SELECT title, author, heat_score, read_count, crawl_timeFROM novel_info WHERE category LIKE '%言情%' OR category LIKE '%romance%'ORDER BY crawl_time DESC, heat_score DESCLIMIT 50"""cursor.execute(romance_query)romance_data = cursor.fetchall()romance_df = pd.DataFrame(romance_data)romance_prediction = {}if len(romance_df) > 0:recent_romance_heat = romance_df['heat_score'].mean()romance_growth_rate = calculate_growth_rate(romance_df)romance_prediction = {'current_avg_heat': float(recent_romance_heat),'predicted_growth': romance_growth_rate,'hot_authors': romance_df.nlargest(5, 'heat_score')[['author', 'heat_score']].to_dict('records'),'trend_keywords': extract_trend_keywords(romance_df)}overall_market_query = """SELECT AVG(heat_score) as market_heat, COUNT(*) as active_novels,COUNT(DISTINCT author) as active_authorsFROM novel_info WHERE crawl_time >= DATE_SUB(NOW(), INTERVAL 1 DAY)"""cursor.execute(overall_market_query)market_data = cursor.fetchone()market_prediction = {'market_activity': 'high' if market_data['market_heat'] > 70 else 'medium' if market_data['market_heat'] > 50 else 'low','active_novels': market_data['active_novels'],'active_authors': market_data['active_authors'],'market_heat_score': float(market_data['market_heat'])}prediction_result = {'category_predictions': category_trends,'romance_special_analysis': romance_prediction,'market_overview': market_prediction,'analysis_timestamp': datetime.now().strftime('%Y-%m-%d %H:%M:%S')}cursor.close()connection.close()return JsonResponse({'status': 'success', 'predictions': prediction_result})

六、项目文档展示

七、总结

基于爬虫技术的网络小说数据分析系统作为一个综合性的毕业设计项目，成功整合了现代Web开发的核心技术栈，实现了从数据采集到分析展示的完整业务流程。项目采用Python爬虫技术自动化收集网络小说平台的热度数据，通过Django框架构建稳定的后端服务架构，结合Vue.js和ElementUI打造了用户友好的前端交互界面。系统不仅具备基础的数据管理功能，还融入了热度分析和趋势预测等智能化特性，为用户提供了有价值的数据洞察。虽然作为学生毕业设计在技术复杂度和业务规模上相对有限，但项目涵盖了完整的软件开发生命周期，从需求分析、系统设计到编码实现都体现了扎实的专业基础。通过这个项目的开发实践，不仅加深了对Python编程、数据库设计、Web开发等核心知识的理解，也培养了解决实际问题的能力和项目管理意识，为今后的专业发展奠定了良好的基础。

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💖🔥作者主页：计算机毕设木哥🔥 💖