【大数据】小红书MCN机构数据可视化分析系统计算机项目 Hadoop+Spark环境配置数据科学与大数据技术附源码+文档+讲解

最新推荐文章于 2025-11-24 22:14:37 发布

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#大数据 #信息可视化 #hadoop #django #spark #python #数据分析

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前言

💖💖作者：计算机程序员小杨
💙💙个人简介：我是一名计算机相关专业的从业者，擅长Java、微信小程序、Python、Golang、安卓Android等多个IT方向。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。热爱技术，喜欢钻研新工具和框架，也乐于通过代码解决实际问题，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！
💕💕文末获取源码联系计算机程序员小杨
💜💜
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
 计算机毕业设计选题
💜💜

一.开发工具简介

大数据框架：Hadoop+Spark（本次没用Hive，支持定制）
开发语言：Python+Java（两个版本都支持）
后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)（两个版本都支持）
前端：Vue+ElementUI+Echarts+HTML+CSS+JavaScript+jQuery
详细技术点：Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy
数据库：MySQL

二.系统内容简介

《小红书MCN机构数据可视化分析系统》是一个基于大数据技术的综合性分析平台，专门针对小红书平台上的MCN机构运营数据进行深度挖掘和可视化呈现。系统采用Hadoop+Spark大数据框架作为核心数据处理引擎，结合Python语言的强大数据分析能力，构建了完整的数据处理链路。后端基于Django框架提供稳定的API服务，前端运用Vue+ElementUI+Echarts技术栈打造直观友好的用户界面。系统通过HDFS分布式存储海量MCN机构数据，利用Spark SQL进行高效的数据查询和分析，结合Pandas、NumPy等科学计算库完成复杂的统计分析任务。平台实现了内容领域分析、地理空间分布分析、机构运营效率分析、机构规模实力分析等核心功能模块，并通过可视化大屏实时展示分析结果，为MCN机构的战略决策和运营优化提供了数据支撑和洞察视角。

三.系统功能演示

【大数据】小红书MCN机构数据可视化分析系统计算机项目 Hadoop+Spark环境配置数据科学与大数据技术附源码+文档+讲解

四.系统界面展示

在这里插入图片描述

五.系统源码展示


from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, avg, sum, desc, asc
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import pandas as pd
import numpy as np
import json

spark = SparkSession.builder.appName("XiaohongshuMCNAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()

def content_domain_analysis(request):
    content_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/mcn_db").option("dbtable", "content_posts").option("user", "root").option("password", "password").load()
    mcn_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/mcn_db").option("dbtable", "mcn_institutions").option("user", "root").option("password", "password").load()
    creator_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/mcn_db").option("dbtable", "content_creators").option("user", "root").option("password", "password").load()
    joined_df = content_df.join(creator_df, "creator_id").join(mcn_df, "mcn_id")
    domain_stats = joined_df.groupBy("mcn_name", "content_domain").agg(count("post_id").alias("post_count"), avg("like_count").alias("avg_likes"), avg("comment_count").alias("avg_comments"), avg("share_count").alias("avg_shares"), sum("view_count").alias("total_views"))
    domain_performance = domain_stats.withColumn("engagement_rate", (col("avg_likes") + col("avg_comments") + col("avg_shares")) / col("total_views") * 100)
    top_domains = domain_performance.groupBy("content_domain").agg(avg("engagement_rate").alias("domain_avg_engagement"), count("mcn_name").alias("mcn_count"), sum("post_count").alias("total_posts")).orderBy(desc("domain_avg_engagement"))
    mcn_domain_ranking = domain_performance.withColumn("domain_score", col("engagement_rate") * 0.4 + col("post_count") * 0.3 + col("total_views") * 0.3).orderBy(desc("domain_score"))
    content_trend_analysis = joined_df.filter(col("post_date") >= "2024-01-01").groupBy("content_domain", "post_month").agg(count("post_id").alias("monthly_posts"), avg("like_count").alias("monthly_avg_likes")).orderBy("content_domain", "post_month")
    cross_domain_creators = creator_df.join(content_df, "creator_id").groupBy("creator_id", "mcn_id").agg(count("content_domain").alias("domain_count")).filter(col("domain_count") > 1)
    domain_competition_index = domain_stats.groupBy("content_domain").agg(count("mcn_name").alias("competing_mcns"), avg("engagement_rate").alias("avg_domain_engagement")).withColumn("competition_intensity", col("competing_mcns") / col("avg_domain_engagement"))
    result_data = {
        "top_domains": top_domains.collect(),
        "mcn_rankings": mcn_domain_ranking.collect(),
        "trend_analysis": content_trend_analysis.collect(),
        "cross_domain_stats": cross_domain_creators.count(),
        "competition_metrics": domain_competition_index.collect()
    }
    return JsonResponse(result_data, safe=False)

def geographic_distribution_analysis(request):
    creator_location_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/mcn_db").option("dbtable", "creator_locations").option("user", "root").option("password", "password").load()
    mcn_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/mcn_db").option("dbtable", "mcn_institutions").option("user", "root").option("password", "password").load()
    content_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/mcn_db").option("dbtable", "content_posts").option("user", "root").option("password", "password").load()
    geo_joined_df = creator_location_df.join(mcn_df, "mcn_id").join(content_df, "creator_id")
    province_distribution = geo_joined_df.groupBy("province", "mcn_name").agg(count("creator_id").alias("creator_count"), avg("follower_count").alias("avg_followers"), sum("total_likes").alias("province_total_likes"))
    city_tier_analysis = geo_joined_df.groupBy("city_tier", "mcn_name").agg(count("creator_id").alias("tier_creator_count"), avg("engagement_rate").alias("tier_avg_engagement"), sum("monthly_income").alias("tier_total_income"))
    regional_performance = province_distribution.withColumn("performance_index", col("avg_followers") * 0.3 + col("province_total_likes") * 0.4 + col("creator_count") * 0.3).orderBy(desc("performance_index"))
    geographic_diversity = geo_joined_df.groupBy("mcn_name").agg(count("province").alias("province_coverage"), count("city_tier").alias("tier_coverage")).withColumn("diversity_score", col("province_coverage") * col("tier_coverage"))
    migration_patterns = geo_joined_df.filter(col("location_change_date").isNotNull()).groupBy("from_province", "to_province").agg(count("creator_id").alias("migration_count")).orderBy(desc("migration_count"))
    market_penetration = province_distribution.groupBy("province").agg(count("mcn_name").alias("mcn_presence"), sum("creator_count").alias("total_creators_in_province")).withColumn("market_saturation", col("mcn_presence") / col("total_creators_in_province"))
    revenue_by_region = geo_joined_df.groupBy("province", "city_tier").agg(sum("monthly_revenue").alias("regional_revenue"), avg("cost_per_acquisition").alias("avg_cpa")).orderBy(desc("regional_revenue"))
    geographic_clustering = geo_joined_df.groupBy("latitude_range", "longitude_range").agg(count("creator_id").alias("cluster_size"), avg("content_quality_score").alias("cluster_quality"))
    seasonal_geographic_trends = geo_joined_df.filter(col("post_date") >= "2024-01-01").groupBy("province", "season").agg(avg("engagement_rate").alias("seasonal_engagement"), count("post_id").alias("seasonal_posts"))
    cross_regional_collaboration = geo_joined_df.join(geo_joined_df.alias("df2"), col("collaboration_id") == col("df2.collaboration_id")).filter(col("province") != col("df2.province")).groupBy("province", "df2.province").agg(count("collaboration_id").alias("cross_regional_projects"))
    result_data = {
        "province_stats": province_distribution.collect(),
        "city_tier_analysis": city_tier_analysis.collect(),
        "performance_rankings": regional_performance.collect(),
        "diversity_metrics": geographic_diversity.collect(),
        "migration_data": migration_patterns.collect(),
        "market_analysis": market_penetration.collect(),
        "revenue_distribution": revenue_by_region.collect()
    }
    return JsonResponse(result_data, safe=False)

def operational_efficiency_analysis(request):
    mcn_operations_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/mcn_db").option("dbtable", "mcn_operations").option("user", "root").option("password", "password").load()
    creator_performance_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/mcn_db").option("dbtable", "creator_performance").option("user", "root").option("password", "password").load()
    campaign_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/mcn_db").option("dbtable", "marketing_campaigns").option("user", "root").option("password", "password").load()
    efficiency_joined_df = mcn_operations_df.join(creator_performance_df, "mcn_id").join(campaign_df, "mcn_id")
    resource_utilization = efficiency_joined_df.groupBy("mcn_name").agg(sum("total_investment").alias("total_invested"), sum("revenue_generated").alias("total_revenue"), count("active_creators").alias("creator_count"), avg("content_production_rate").alias("avg_production_rate"))
    roi_analysis = resource_utilization.withColumn("roi_percentage", (col("total_revenue") - col("total_invested")) / col("total_invested") * 100).withColumn("revenue_per_creator", col("total_revenue") / col("creator_count"))
    operational_metrics = efficiency_joined_df.groupBy("mcn_name").agg(avg("campaign_completion_time").alias("avg_completion_time"), avg("client_satisfaction_score").alias("avg_satisfaction"), count("successful_campaigns").alias("success_count"), count("total_campaigns").alias("total_campaigns"))
    efficiency_score = operational_metrics.withColumn("success_rate", col("success_count") / col("total_campaigns") * 100).withColumn("efficiency_index", col("success_rate") * 0.4 + col("avg_satisfaction") * 0.3 + (100 / col("avg_completion_time")) * 0.3)
    cost_effectiveness = efficiency_joined_df.groupBy("mcn_name").agg(sum("operational_costs").alias("total_costs"), sum("marketing_spend").alias("marketing_costs"), avg("cost_per_acquisition").alias("avg_cpa"), avg("lifetime_value").alias("avg_ltv"))
    profit_margins = cost_effectiveness.withColumn("profit_margin", (col("total_revenue") - col("total_costs")) / col("total_revenue") * 100).withColumn("ltv_cpa_ratio", col("avg_ltv") / col("avg_cpa"))
    productivity_trends = efficiency_joined_df.filter(col("operation_date") >= "2024-01-01").groupBy("mcn_name", "operation_month").agg(avg("daily_content_output").alias("monthly_output"), avg("team_productivity_score").alias("monthly_productivity"))
    benchmark_comparison = efficiency_score.withColumn("industry_rank", row_number().over(Window.orderBy(desc("efficiency_index")))).withColumn("percentile_rank", percent_rank().over(Window.orderBy("efficiency_index")))
    automation_impact = efficiency_joined_df.groupBy("mcn_name", "automation_level").agg(avg("processing_time").alias("avg_processing_time"), avg("error_rate").alias("avg_error_rate"), sum("cost_savings").alias("automation_savings"))
    scalability_metrics = efficiency_joined_df.groupBy("mcn_name").agg(max("peak_capacity").alias("max_capacity"), avg("current_utilization").alias("avg_utilization")).withColumn("scalability_potential", col("max_capacity") - col("avg_utilization"))
    result_data = {
        "roi_metrics": roi_analysis.collect(),
        "efficiency_scores": efficiency_score.collect(),
        "cost_analysis": profit_margins.collect(),
        "productivity_data": productivity_trends.collect(),
        "industry_benchmarks": benchmark_comparison.collect(),
        "automation_metrics": automation_impact.collect(),
        "scalability_data": scalability_metrics.collect()
    }
    return JsonResponse(result_data, safe=False)