💖💖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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1688商品类目关系分析与可视化系统介绍
《基于大数据的1688商品类目关系分析与可视化系统》是一个专门针对电商平台商品数据进行深度分析的综合性大数据处理系统，该系统采用了先进的Hadoop分布式存储架构结合Spark大数据计算引擎，能够高效处理海量的1688平台商品类目数据。系统在技术实现上支持Python+Django和Java+SpringBoot两套完整的开发方案，前端采用Vue+ElementUI+Echarts技术栈打造现代化的交互界面，通过MySQL数据库实现数据的持久化存储。系统核心功能涵盖了全站宏观结构分析，能够从整体维度展现1688平台的商品分布格局；核心类目特征分析功能运用Pandas和NumPy等数据科学库对关键商品类目进行深入的统计分析和特征提取；类目层级关系分析模块通过Spark SQL进行复杂的关系型数据查询，构建出完整的商品类目层级树状结构；类目关联模式分析则运用大数据挖掘算法发现不同类目之间的潜在关联规律和商业价值。系统还特别设计了大屏可视化功能，通过Echarts图表库将分析结果以直观的图表形式展现，支持多维度的数据可视化呈现，同时配备了完善的用户管理体系和个人信息管理模块，为用户提供了一个功能全面、技术先进、界面友好的大数据商品类目分析平台。
1688商品类目关系分析与可视化系统演示视频
1688商品类目关系分析与可视化系统演示图片
1688商品类目关系分析与可视化系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, sum as spark_sum, avg, desc, asc, when, isnan, isnull
from pyspark.sql.types import StringType, IntegerType, DoubleType
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
spark = SparkSession.builder.appName("1688CategoryAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def macro_structure_analysis(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/category_db").option("dbtable", "category_info").option("user", "root").option("password", "123456").load()
total_categories = df.count()
level_distribution = df.groupBy("category_level").agg(count("*").alias("count")).orderBy(asc("category_level"))
level_stats = level_distribution.collect()
category_tree = df.groupBy("parent_category").agg(count("category_id").alias("child_count")).orderBy(desc("child_count"))
top_parent_categories = category_tree.limit(20).collect()
avg_products_per_category = df.agg(avg("product_count").alias("avg_count")).collect()[0]["avg_count"]
product_distribution = df.select("category_name", "product_count").orderBy(desc("product_count"))
hot_categories = product_distribution.limit(50).collect()
category_coverage = df.filter(col("product_count") > 0).count() / total_categories * 100
empty_categories = df.filter(col("product_count") == 0).count()
max_depth = df.agg({"category_level": "max"}).collect()[0][0]
min_depth = df.agg({"category_level": "min"}).collect()[0][0]
depth_range = max_depth - min_depth
category_density = df.groupBy("category_level").agg(avg("product_count").alias("avg_density")).orderBy("category_level")
density_stats = category_density.collect()
result_data = {"total_categories": total_categories, "level_distribution": [{"level": row["category_level"], "count": row["count"]} for row in level_stats], "top_parent_categories": [{"parent": row["parent_category"], "child_count": row["child_count"]} for row in top_parent_categories], "avg_products_per_category": round(avg_products_per_category, 2), "hot_categories": [{"name": row["category_name"], "product_count": row["product_count"]} for row in hot_categories], "category_coverage": round(category_coverage, 2), "empty_categories": empty_categories, "depth_range": depth_range, "density_stats": [{"level": row["category_level"], "density": round(row["avg_density"], 2)} for row in density_stats]}
return JsonResponse({"status": "success", "data": result_data})
def core_category_feature_analysis(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/category_db").option("dbtable", "category_detail").option("user", "root").option("password", "123456").load()
core_categories = df.filter(col("is_core") == 1)
feature_stats = core_categories.groupBy("category_type").agg(count("*").alias("type_count"), avg("product_count").alias("avg_products"), spark_sum("product_count").alias("total_products"), avg("supplier_count").alias("avg_suppliers"))
type_analysis = feature_stats.collect()
price_range_analysis = core_categories.groupBy("price_range").agg(count("*").alias("range_count"), avg("avg_price").alias("avg_range_price")).orderBy("price_range")
price_stats = price_range_analysis.collect()
popularity_score = core_categories.withColumn("popularity", (col("product_count") * 0.4 + col("supplier_count") * 0.3 + col("search_volume") * 0.3))
top_popular = popularity_score.select("category_name", "popularity", "product_count", "supplier_count", "search_volume").orderBy(desc("popularity")).limit(30)
popular_categories = top_popular.collect()
growth_trend = core_categories.withColumn("growth_rate", ((col("current_month_products") - col("last_month_products")) / col("last_month_products") * 100))
growing_categories = growth_trend.filter(col("growth_rate") > 0).select("category_name", "growth_rate").orderBy(desc("growth_rate")).limit(20)
growth_stats = growing_categories.collect()
competition_analysis = core_categories.withColumn("competition_index", (col("supplier_count") / col("product_count") * 100))
competition_levels = competition_analysis.groupBy(when(col("competition_index") < 10, "Low").when(col("competition_index") < 30, "Medium").otherwise("High").alias("competition_level")).count()
competition_stats = competition_levels.collect()
seasonal_pattern = core_categories.select("category_name", "jan_sales", "apr_sales", "jul_sales", "oct_sales")
seasonal_variance = seasonal_pattern.withColumn("variance", ((col("jul_sales") - col("jan_sales")) / col("jan_sales") * 100))
seasonal_categories = seasonal_variance.filter(abs(col("variance")) > 20).select("category_name", "variance").orderBy(desc(abs(col("variance"))))
seasonal_stats = seasonal_categories.limit(25).collect()
result_data = {"type_analysis": [{"type": row["category_type"], "count": row["type_count"], "avg_products": round(row["avg_products"], 2), "total_products": row["total_products"], "avg_suppliers": round(row["avg_suppliers"], 2)} for row in type_analysis], "price_stats": [{"range": row["price_range"], "count": row["range_count"], "avg_price": round(row["avg_range_price"], 2)} for row in price_stats], "popular_categories": [{"name": row["category_name"], "popularity": round(row["popularity"], 2), "products": row["product_count"], "suppliers": row["supplier_count"]} for row in popular_categories], "growth_stats": [{"name": row["category_name"], "growth_rate": round(row["growth_rate"], 2)} for row in growth_stats], "competition_stats": [{"level": row["competition_level"], "count": row["count"]} for row in competition_stats], "seasonal_stats": [{"name": row["category_name"], "variance": round(row["variance"], 2)} for row in seasonal_stats]}
return JsonResponse({"status": "success", "data": result_data})
def category_association_pattern_analysis(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/category_db").option("dbtable", "category_association").option("user", "root").option("password", "123456").load()
association_rules = df.filter((col("confidence") >= 0.3) & (col("support") >= 0.1) & (col("lift") >= 1.2))
strong_associations = association_rules.select("category_a", "category_b", "support", "confidence", "lift").orderBy(desc("lift"))
top_associations = strong_associations.limit(100).collect()
category_clusters = df.groupBy("cluster_id").agg(count("category_id").alias("cluster_size"), avg("support").alias("avg_support"), avg("confidence").alias("avg_confidence"))
cluster_analysis = category_clusters.orderBy(desc("cluster_size")).collect()
cross_level_patterns = df.filter(col("level_a") != col("level_b")).groupBy("level_a", "level_b").agg(count("*").alias("pattern_count"), avg("confidence").alias("avg_confidence"))
cross_level_stats = cross_level_patterns.orderBy(desc("pattern_count")).collect()
frequent_itemsets = df.filter(col("support") >= 0.15).groupBy("itemset_size").agg(count("*").alias("itemset_count"), avg("support").alias("avg_support"))
itemset_stats = frequent_itemsets.orderBy("itemset_size").collect()
temporal_patterns = df.filter(col("time_period").isNotNull()).groupBy("time_period").agg(count("*").alias("period_count"), avg("confidence").alias("period_confidence"))
temporal_stats = temporal_patterns.orderBy("time_period").collect()
geographic_patterns = df.filter(col("region").isNotNull()).groupBy("region").agg(count("*").alias("region_patterns"), avg("lift").alias("avg_lift"))
geographic_stats = geographic_patterns.orderBy(desc("avg_lift")).collect()
supplier_co_occurrence = df.filter(col("supplier_overlap") > 0).groupBy("category_a", "category_b").agg(avg("supplier_overlap").alias("overlap_rate"))
supplier_patterns = supplier_co_occurrence.filter(col("overlap_rate") > 0.2).orderBy(desc("overlap_rate")).limit(50)
supplier_stats = supplier_patterns.collect()
price_correlation = df.filter((col("price_a").isNotNull()) & (col("price_b").isNotNull())).select("category_a", "category_b", "price_a", "price_b")
price_corr_pandas = price_correlation.toPandas()
if len(price_corr_pandas) > 0:
correlation_matrix = np.corrcoef(price_corr_pandas["price_a"], price_corr_pandas["price_b"])[0, 1]
else:
correlation_matrix = 0
market_penetration = df.groupBy("category_a").agg(count("category_b").alias("connected_categories"), avg("confidence").alias("avg_penetration"))
penetration_stats = market_penetration.orderBy(desc("connected_categories")).limit(40).collect()
result_data = {"top_associations": [{"category_a": row["category_a"], "category_b": row["category_b"], "support": round(row["support"], 3), "confidence": round(row["confidence"], 3), "lift": round(row["lift"], 3)} for row in top_associations], "cluster_analysis": [{"cluster_id": row["cluster_id"], "size": row["cluster_size"], "avg_support": round(row["avg_support"], 3), "avg_confidence": round(row["avg_confidence"], 3)} for row in cluster_analysis], "cross_level_stats": [{"level_a": row["level_a"], "level_b": row["level_b"], "count": row["pattern_count"], "confidence": round(row["avg_confidence"], 3)} for row in cross_level_stats], "itemset_stats": [{"size": row["itemset_size"], "count": row["itemset_count"], "support": round(row["avg_support"], 3)} for row in itemset_stats], "temporal_stats": [{"period": row["time_period"], "count": row["period_count"], "confidence": round(row["period_confidence"], 3)} for row in temporal_stats], "geographic_stats": [{"region": row["region"], "patterns": row["region_patterns"], "lift": round(row["avg_lift"], 3)} for row in geographic_stats], "supplier_stats": [{"category_a": row["category_a"], "category_b": row["category_b"], "overlap": round(row["overlap_rate"], 3)} for row in supplier_stats], "price_correlation": round(correlation_matrix, 3), "penetration_stats": [{"category": row["category_a"], "connections": row["connected_categories"], "penetration": round(row["avg_penetration"], 3)} for row in penetration_stats]}
return JsonResponse({"status": "success", "data": result_data})
1688商品类目关系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
