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💖💖作者：计算机毕业设计小途
💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！
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豆瓣图书数据分析与可视化系统介绍

《基于大数据的豆瓣图书数据分析与可视化》是一个面向豆瓣图书数据的毕业设计系统，主要用Hadoop和Spark做大数据处理，底层通过HDFS保存原始图书数据，用Spark SQL完成清洗、分组、统计和聚合，再结合Pandas、NumPy做指标计算，分析结果落到MySQL中，后端可选Python+Django或Java+Spring Boot实现接口，前端用Vue、ElementUI、ECharts等展示图表。系统功能包括系统首页、大屏可视化、豆瓣图书分析、类型分布、创作状态、作者产出、风格偏好、作品热度、时间趋势、群体画像、用户管理、小说信息、个人信息和修改密码等。它把图书类型、作者产出、作品热度、评分变化、出版时间趋势和读者群体特征这些内容整理成图表和指标，帮助使用者从大数据角度了解豆瓣图书的整体情况。大屏可视化可以集中展示核心统计结果，普通分析页面则按主题查看类型分布、风格偏好、作品热度和时间趋势，用户模块负责登录后的个人信息维护和密码修改。整体上，这个系统把数据采集后的离线分析、指标统计、结果存储和可视化展示串成一条完整流程。

豆瓣图书数据分析与可视化系统演示视频

项目演示视频

豆瓣图书数据分析与可视化系统演示图片

豆瓣图书数据分析与可视化系统代码展示

spark=SparkSession.builder.appName("DoubanBookBigData").master("local[*]").config("spark.sql.shuffle.partitions","4").getOrCreate()defanalyze_type_distribution():book_df=spark.read.option("header","true").option("inferSchema","true").csv("hdfs:///douban/book/books.csv")book_df=book_df.filter("type is not null and type <> ''")book_df.cache()book_df.createOrReplaceTempView("douban_book")type_df=spark.sql("SELECT type, COUNT(*) AS book_count, ROUND(AVG(rating), 2) AS avg_rating FROM douban_book GROUP BY type ORDER BY book_count DESC")type_df=type_df.filter("book_count > 0")pdf=type_df.toPandas()pdf["percent"]=(pdf["book_count"]/pdf["book_count"].sum()*100).round(2)pdf["rank_no"]=pdf["book_count"].rank(method="dense",ascending=False).astype(int)pdf["avg_rating"]=pdf["avg_rating"].fillna(0).round(2)records=pdf.to_dict(orient="records")cursor=mysql_conn.cursor()cursor.execute("DELETE FROM book_type_stat")cursor.executemany("REPLACE INTO book_type_stat(type_name, book_count, avg_rating, percent, rank_no) VALUES(%s, %s, %s, %s, %s)",[(r["type"],int(r["book_count"]),float(r["avg_rating"]),float(r["percent"]),int(r["rank_no"]))forrinrecords])mysql_conn.commit()cursor.close()book_df.unpersist()return{"code":200,"data":records,"total":len(records)}defanalyze_book_hot():hot_df=spark.read.option("header","true").option("inferSchema","true").csv("hdfs:///douban/book/books.csv")hot_df=hot_df.filter("rating is not null and rating_count > 0 and comment_count is not null")hot_df.createOrReplaceTempView("book_hot")rank_df=spark.sql("SELECT title, author, rating, rating_count, comment_count, (rating * 0.6 + LOG(rating_count + 1) * 0.25 + LOG(comment_count + 1) * 0.15) AS hot_score FROM book_hot ORDER BY hot_score DESC LIMIT 50")rank_df=rank_df.filter("hot_score is not null")pdf=rank_df.toPandas()pdf["rating"]=pdf["rating"].round(2)pdf["hot_score"]=pdf["hot_score"].round(2)pdf["hot_level"]=numpy.where(pdf["hot_score"]>=pdf["hot_score"].quantile(0.8),"高热",numpy.where(pdf["hot_score"]>=pdf["hot_score"].quantile(0.5),"中热","普通"))pdf["hot_rank"]=pdf["hot_score"].rank(method="min",ascending=False).astype(int)records=pdf.to_dict(orient="records")cursor=mysql_conn.cursor()cursor.execute("DELETE FROM book_hot_rank")cursor.executemany("REPLACE INTO book_hot_rank(title, author, rating, rating_count, comment_count, hot_score, hot_level, hot_rank) VALUES(%s, %s, %s, %s, %s, %s, %s, %s)",[(r["title"],r["author"],float(r["rating"]),int(r["rating_count"]),int(r["comment_count"]),float(r["hot_score"]),r["hot_level"],int(r["hot_rank"]))forrinrecords])mysql_conn.commit()cursor.close()return{"code":200,"data":records,"total":len(records)}defanalyze_time_trend():trend_df=spark.read.option("header","true").option("inferSchema","true").csv("hdfs:///douban/book/books.csv")trend_df=trend_df.filter("publish_year is not null and rating_count is not null")trend_df.createOrReplaceTempView("book_trend")year_df=spark.sql("SELECT publish_year, COUNT(*) AS publish_count, ROUND(AVG(rating), 2) AS avg_rating, SUM(rating_count) AS total_rating_count FROM book_trend WHERE publish_year BETWEEN 2000 AND 2026 GROUP BY publish_year ORDER BY publish_year")year_df=year_df.filter("publish_count > 0")pdf=year_df.toPandas()pdf=pdf.sort_values("publish_year")pdf["moving_avg"]=pdf["publish_count"].rolling(window=3,min_periods=1).mean().round(2)pdf["diff_value"]=pdf["moving_avg"].diff().fillna(0).round(2)pdf["trend_flag"]=numpy.where(pdf["diff_value"]>0,"上升",numpy.where(pdf["diff_value"]<0,"下降","平稳"))pdf["avg_rating"]=pdf["avg_rating"].fillna(0).round(2)records=pdf.to_dict(orient="records")cursor=mysql_conn.cursor()cursor.execute("DELETE FROM book_time_trend")cursor.executemany("REPLACE INTO book_time_trend(publish_year, publish_count, avg_rating, total_rating_count, moving_avg, trend_flag) VALUES(%s, %s, %s, %s, %s, %s)",[(int(r["publish_year"]),int(r["publish_count"]),float(r["avg_rating"]),int(r["total_rating_count"]),float(r["moving_avg"]),r["trend_flag"])forrinrecords])mysql_conn.commit()cursor.close()return{"code":200,"data":records,"total":len(records)}

豆瓣图书数据分析与可视化系统文档展示

💖💖作者：计算机毕业设计小途
💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！
💛💛想说的话：感谢大家的关注与支持！
💜💜
网站实战项目
安卓/小程序实战项目
大数据实战项目
深度学习实战项目