💖💖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
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基于大数据的小红书MCN机构数据可视化分析系统介绍
《基于大数据的小红书MCN机构数据可视化分析系统》是一套采用现代大数据技术栈构建的综合性数据分析平台，该系统以Hadoop分布式文件系统HDFS作为数据存储基础，结合Apache Spark大数据计算引擎实现海量数据的高效处理与分析。系统架构采用前后端分离设计模式，后端基于Spring Boot框架构建RESTful API服务接口，同时提供Django版本以满足Python技术栈需求，前端采用Vue.js响应式框架配合ElementUI组件库打造现代化用户交互界面，通过Echarts数据可视化库实现丰富的图表展示效果。系统核心功能模块包括用户管理体系（涵盖个人信息维护、密码修改等基础功能）、大屏数据可视化展示中心、内容领域深度分析模块、地理空间分布统计分析、MCN机构运营效率评估以及机构规模实力综合评价等六大核心板块。在数据处理层面，系统充分利用Spark SQL进行结构化数据查询分析，结合Pandas和NumPy等Python科学计算库实现复杂的数据清洗与统计建模工作，所有处理结果统一存储于MySQL关系型数据库中确保数据一致性与查询性能。整个系统通过整合小红书平台MCN机构的多维度运营数据，为用户提供直观清晰的数据洞察与决策支持，充分体现了大数据技术在社交媒体数据分析领域的实际应用价值。
基于大数据的小红书MCN机构数据可视化分析系统演示视频
基于大数据的小红书MCN机构数据可视化分析系统演示图片
基于大数据的小红书MCN机构数据可视化分析系统代码展示
spark = SparkSession.builder.appName("XiaohongshuMCNAnalysis").config("spark.some.config.option", "some-value").getOrCreate()
def analyze_content_field_distribution():
mcn_content_df = spark.sql("SELECT mcn_id, content_field, publish_count, like_count, comment_count, share_count FROM mcn_content_data")
field_stats = mcn_content_df.groupBy("content_field").agg(
spark_sum("publish_count").alias("total_publish"),
spark_sum("like_count").alias("total_likes"),
spark_sum("comment_count").alias("total_comments"),
spark_sum("share_count").alias("total_shares"),
count("mcn_id").alias("mcn_count")
)
engagement_rate = field_stats.withColumn("avg_engagement",
(col("total_likes") + col("total_comments") + col("total_shares")) / col("total_publish"))
field_performance = engagement_rate.withColumn("performance_score",
col("avg_engagement") * 0.6 + col("mcn_count") * 0.4)
top_fields = field_performance.orderBy(col("performance_score").desc()).limit(10)
field_trend_data = mcn_content_df.withColumn("publish_month",
date_format(col("publish_date"), "yyyy-MM"))
monthly_trend = field_trend_data.groupBy("content_field", "publish_month").agg(
spark_sum("publish_count").alias("monthly_publish"),
avg("like_count").alias("avg_monthly_likes")
)
growth_rate = monthly_trend.withColumn("growth_rate",
(col("monthly_publish") - lag("monthly_publish", 1).over(
Window.partitionBy("content_field").orderBy("publish_month"))) /
lag("monthly_publish", 1).over(Window.partitionBy("content_field").orderBy("publish_month")) * 100)
result_data = top_fields.join(growth_rate.groupBy("content_field").agg(
avg("growth_rate").alias("avg_growth_rate")), "content_field", "left")
final_results = result_data.select("content_field", "total_publish", "avg_engagement",
"performance_score", "avg_growth_rate").collect()
return [{"field": row.content_field, "publish_count": row.total_publish,
"engagement": float(row.avg_engagement), "score": float(row.performance_score),
"growth": float(row.avg_growth_rate) if row.avg_growth_rate else 0.0} for row in final_results]
def analyze_geographic_distribution():
mcn_location_df = spark.sql("SELECT mcn_id, province, city, creator_count, total_fans, avg_engagement FROM mcn_geographic_data")
province_stats = mcn_location_df.groupBy("province").agg(
count("mcn_id").alias("mcn_count"),
spark_sum("creator_count").alias("total_creators"),
spark_sum("total_fans").alias("total_province_fans"),
avg("avg_engagement").alias("avg_province_engagement")
)
province_density = province_stats.withColumn("creator_density",
col("total_creators") / col("mcn_count"))
province_influence = province_density.withColumn("influence_index",
col("total_province_fans") * 0.4 + col("avg_province_engagement") * 0.6)
city_distribution = mcn_location_df.groupBy("province", "city").agg(
count("mcn_id").alias("city_mcn_count"),
spark_sum("creator_count").alias("city_creators"),
avg("total_fans").alias("avg_city_fans")
)
city_ranking = city_distribution.withColumn("city_score",
col("city_mcn_count") * 0.3 + col("city_creators") * 0.4 + col("avg_city_fans") * 0.3)
top_cities = city_ranking.orderBy(col("city_score").desc()).limit(20)
regional_comparison = province_stats.withColumn("development_level",
when(col("mcn_count") >= 50, "高度发达")
.when(col("mcn_count") >= 20, "中等发达")
.otherwise("发展中"))
geographic_heatmap = mcn_location_df.select("province", "city", "mcn_id").groupBy("province", "city").count()
concentration_index = geographic_heatmap.agg(
((spark_sum(col("count") * col("count")) / spark_sum("count")) - 1) /
(count("*") - 1)).alias("concentration")
province_results = province_influence.orderBy(col("influence_index").desc()).collect()
city_results = top_cities.collect()
return {"provinces": [{"name": row.province, "mcn_count": row.mcn_count,
"creator_density": float(row.creator_density),
"influence_index": float(row.influence_index)} for row in province_results],
"cities": [{"province": row.province, "city": row.city,
"mcn_count": row.city_mcn_count, "score": float(row.city_score)} for row in city_results]}
def analyze_mcn_operation_efficiency():
mcn_operation_df = spark.sql("SELECT mcn_id, creator_count, content_output, revenue, cost, operation_months FROM mcn_operation_data")
efficiency_metrics = mcn_operation_df.withColumn("content_per_creator",
col("content_output") / col("creator_count"))
efficiency_metrics = efficiency_metrics.withColumn("revenue_per_creator",
col("revenue") / col("creator_count"))
efficiency_metrics = efficiency_metrics.withColumn("roi_ratio",
(col("revenue") - col("cost")) / col("cost"))
efficiency_metrics = efficiency_metrics.withColumn("monthly_efficiency",
col("content_output") / col("operation_months"))
efficiency_categories = efficiency_metrics.withColumn("efficiency_level",
when(col("roi_ratio") >= 0.5, "高效运营")
.when(col("roi_ratio") >= 0.2, "中等效率")
.otherwise("待优化"))
cost_analysis = efficiency_metrics.withColumn("cost_per_content",
col("cost") / col("content_output"))
cost_analysis = cost_analysis.withColumn("cost_efficiency_score",
100 - (col("cost_per_content") / avg("cost_per_content").over(Window.partitionBy()) * 100))
operation_stability = mcn_operation_df.withColumn("stability_score",
col("operation_months") * 0.3 + (col("revenue") / col("cost")) * 0.7)
comprehensive_score = efficiency_metrics.join(cost_analysis.select("mcn_id", "cost_efficiency_score"), "mcn_id")
comprehensive_score = comprehensive_score.join(operation_stability.select("mcn_id", "stability_score"), "mcn_id")
final_efficiency = comprehensive_score.withColumn("overall_efficiency",
col("roi_ratio") * 0.4 + col("cost_efficiency_score") * 0.3 + col("stability_score") * 0.3)
efficiency_ranking = final_efficiency.orderBy(col("overall_efficiency").desc())
benchmark_analysis = efficiency_ranking.agg(
avg("content_per_creator").alias("avg_content_per_creator"),
avg("revenue_per_creator").alias("avg_revenue_per_creator"),
avg("roi_ratio").alias("avg_roi"),
percentile_approx("overall_efficiency", 0.75).alias("top_25_percentile")
)
top_performers = efficiency_ranking.filter(col("overall_efficiency") >=
benchmark_analysis.select("top_25_percentile").collect()[0][0]).collect()
efficiency_distribution = efficiency_categories.groupBy("efficiency_level").count().collect()
return {"top_mcns": [{"mcn_id": row.mcn_id, "efficiency_score": float(row.overall_efficiency),
"roi": float(row.roi_ratio), "content_rate": float(row.content_per_creator)} for row in top_performers],
"distribution": [{"level": row.efficiency_level, "count": row.count} for row in efficiency_distribution]}
基于大数据的小红书MCN机构数据可视化分析系统文档展示
💖💖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
