💖💖作者：计算机毕业设计杰瑞 💙💙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 深度学校实战项目 计算机毕业设计选题推荐
B站数据分析可视化系统介绍
B站数据分析可视化系统是一个基于大数据技术栈构建的综合性数据处理与分析平台，专门针对B站海量视频数据进行深度挖掘和可视化展示。系统采用Hadoop分布式存储架构作为数据底层支撑，通过HDFS实现大规模视频数据的可靠存储和高效访问，同时运用Spark大数据处理引擎进行实时数据计算和批处理分析。后端技术方面，系统支持Python Django和Java Spring Boot双重开发框架，能够灵活应对不同的业务场景需求，其中Django版本更适合快速原型开发，而Spring Boot版本则在企业级应用中表现更加稳定。前端采用Vue.js框架配合ElementUI组件库构建用户交互界面，通过Echarts图表库实现数据的动态可视化展示，支持多维度数据图表呈现。系统核心功能涵盖视频热度分析、用户行为追踪、热门内容预测等模块，能够从不同角度解析B站平台的内容生态，为内容创作者和平台运营提供数据支撑。整个系统架构采用前后端分离设计，通过RESTful API实现数据交互，保证了系统的可扩展性和维护性。# B站数据分析可视化系统演示视频
B站数据分析可视化系统演示视频
B站数据分析可视化系统演示图片
B站数据分析可视化系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, desc, avg, sum, when, row_number, rank
from pyspark.sql.window import Window
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.regression import LinearRegression
import pandas as pd
import numpy as np
spark = SparkSession.builder.appName("BilibiliDataAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def analyze_hot_videos():
video_data = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/bilibili").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "videos").option("user", "root").option("password", "password").load()
user_behavior = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/bilibili").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "user_actions").option("user", "root").option("password", "password").load()
joined_data = video_data.join(user_behavior, video_data.video_id == user_behavior.video_id, "inner")
video_stats = joined_data.groupBy("video_id", "title", "category", "upload_time").agg(count("action_type").alias("total_interactions"), sum(when(col("action_type") == "like", 1).otherwise(0)).alias("likes"), sum(when(col("action_type") == "comment", 1).otherwise(0)).alias("comments"), sum(when(col("action_type") == "share", 1).otherwise(0)).alias("shares"))
video_stats = video_stats.withColumn("engagement_score", col("likes") * 3 + col("comments") * 5 + col("shares") * 8)
window_spec = Window.partitionBy("category").orderBy(desc("engagement_score"))
ranked_videos = video_stats.withColumn("category_rank", row_number().over(window_spec))
hot_videos = ranked_videos.filter(col("category_rank") <= 10).select("video_id", "title", "category", "engagement_score", "likes", "comments", "shares", "category_rank")
result_df = hot_videos.toPandas()
category_summary = video_stats.groupBy("category").agg(avg("engagement_score").alias("avg_engagement"), count("video_id").alias("video_count"), sum("likes").alias("total_likes")).orderBy(desc("avg_engagement"))
time_trend = joined_data.withColumn("hour", expr("hour(upload_time)")).groupBy("hour").agg(count("video_id").alias("video_count"), avg("engagement_score").alias("avg_engagement")).orderBy("hour")
hot_videos.write.mode("overwrite").format("jdbc").option("url", "jdbc:mysql://localhost:3306/bilibili").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "hot_videos_analysis").option("user", "root").option("password", "password").save()
return result_df.to_dict('records')
def predict_video_popularity():
training_data = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/bilibili").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "video_features").option("user", "root").option("password", "password").load()
feature_data = training_data.select("video_id", "duration", "tag_count", "description_length", "upload_hour", "uploader_followers", "historical_avg_views", "title_length", "thumbnail_quality", "engagement_score")
cleaned_data = feature_data.filter(col("duration").isNotNull() & col("tag_count").isNotNull() & col("engagement_score").isNotNull())
feature_columns = ["duration", "tag_count", "description_length", "upload_hour", "uploader_followers", "historical_avg_views", "title_length", "thumbnail_quality"]
assembler = VectorAssembler(inputCols=feature_columns, outputCol="features")
assembled_data = assembler.transform(cleaned_data)
training_set, test_set = assembled_data.randomSplit([0.8, 0.2], seed=42)
lr_model = LinearRegression(featuresCol="features", labelCol="engagement_score", regParam=0.1, elasticNetParam=0.8)
trained_model = lr_model.fit(training_set)
predictions = trained_model.transform(test_set)
model_metrics = trained_model.evaluate(test_set)
rmse_value = model_metrics.rootMeanSquaredError
r2_value = model_metrics.r2
new_videos = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/bilibili").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "pending_videos").option("user", "root").option("password", "password").load()
new_features = assembler.transform(new_videos)
popularity_predictions = trained_model.transform(new_features)
prediction_results = popularity_predictions.select("video_id", "title", "prediction").withColumnRenamed("prediction", "predicted_popularity")
top_predictions = prediction_results.orderBy(desc("predicted_popularity")).limit(20)
prediction_results.write.mode("overwrite").format("jdbc").option("url", "jdbc:mysql://localhost:3306/bilibili").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "popularity_predictions").option("user", "root").option("password", "password").save()
return top_predictions.toPandas().to_dict('records')
def analyze_user_behavior():
user_actions = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/bilibili").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "user_actions").option("user", "root").option("password", "password").load()
user_profiles = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/bilibili").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "users").option("user", "root").option("password", "password").load()
video_info = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/bilibili").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "videos").option("user", "root").option("password", "password").load()
user_video_data = user_actions.join(video_info, "video_id").join(user_profiles, "user_id")
user_activity = user_video_data.groupBy("user_id", "age_group", "registration_date").agg(count("action_type").alias("total_actions"), countDistinct("video_id").alias("unique_videos"), sum(when(col("action_type") == "like", 1).otherwise(0)).alias("like_count"), sum(when(col("action_type") == "comment", 1).otherwise(0)).alias("comment_count"), sum(when(col("action_type") == "share", 1).otherwise(0)).alias("share_count"))
user_activity = user_activity.withColumn("activity_score", col("like_count") * 1 + col("comment_count") * 3 + col("share_count") * 5)
category_preferences = user_video_data.groupBy("user_id", "category").agg(count("action_type").alias("interactions")).withColumn("rank", row_number().over(Window.partitionBy("user_id").orderBy(desc("interactions")))).filter(col("rank") <= 3)
time_patterns = user_video_data.withColumn("action_hour", expr("hour(action_time)")).groupBy("user_id", "action_hour").agg(count("action_type").alias("hourly_actions"))
peak_hours = time_patterns.withColumn("hour_rank", row_number().over(Window.partitionBy("user_id").orderBy(desc("hourly_actions")))).filter(col("hour_rank") <= 2)
user_segments = user_activity.withColumn("user_segment", when(col("activity_score") >= 100, "highly_active").when(col("activity_score") >= 50, "moderately_active").otherwise("low_active"))
engagement_by_age = user_video_data.groupBy("age_group").agg(avg(when(col("action_type") == "like", 1).otherwise(0)).alias("avg_like_rate"), avg(when(col("action_type") == "comment", 1).otherwise(0)).alias("avg_comment_rate"), count("user_id").alias("user_count"))
content_interaction = user_video_data.groupBy("category", "action_type").agg(count("user_id").alias("interaction_count")).groupBy("category").pivot("action_type").agg(sum("interaction_count"))
user_segments.write.mode("overwrite").format("jdbc").option("url", "jdbc:mysql://localhost:3306/bilibili").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "user_behavior_analysis").option("user", "root").option("password", "password").save()
return user_segments.toPandas().to_dict('records')
B站数据分析可视化系统文档展示
💖💖作者：计算机毕业设计杰瑞 💙💙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 深度学校实战项目 计算机毕业设计选题推荐
