💖💖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
基于大数据的旅游网站用户行为数据分析系统介绍
旅游网站用户行为数据分析系统是一个基于大数据技术架构的综合性分析平台，采用Hadoop+Spark+Django三大核心技术栈构建。该系统专门针对旅游行业用户在网站上的各种行为数据进行深度分析和挖掘，通过Hadoop分布式文件系统存储海量用户行为日志，利用Spark强大的内存计算能力对数据进行实时处理和分析，结合Django框架提供稳定可靠的Web服务。系统前端采用Vue+ElementUI+Echarts技术栈，为用户提供直观友好的数据可视化界面，支持多维度数据图表展示。核心功能模块包括系统首页、个人中心、用户管理、数据分析和系统管理五大板块，能够对用户的浏览轨迹、停留时间、点击热点、搜索关键词等行为数据进行统计分析，为旅游企业的运营决策提供数据支撑。系统采用MySQL作为关系型数据库存储结构化数据，通过Spark SQL实现高效的数据查询和处理，整体架构设计合理，技术选型恰当，具备良好的扩展性和实用性。
基于大数据的旅游网站用户行为数据分析系统演示视频
基于大数据的旅游网站用户行为数据分析系统演示图片
基于大数据的旅游网站用户行为数据分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, sum, avg, max, min, when, desc, asc
from pyspark.sql.types import *
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
import mysql.connector
from datetime import datetime, timedelta
spark = SparkSession.builder.appName("TravelWebsiteUserBehaviorAnalysis").config("spark.some.config.option", "some-value").getOrCreate()
@csrf_exempt
def user_behavior_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
start_date = data.get('start_date')
end_date = data.get('end_date')
behavior_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/travel_db").option("dbtable", "user_behavior").option("user", "root").option("password", "password").load()
filtered_df = behavior_df.filter((col("behavior_date") >= start_date) & (col("behavior_date") <= end_date))
page_view_stats = filtered_df.filter(col("behavior_type") == "page_view").groupBy("page_url").agg(count("user_id").alias("pv_count"), count("user_id").alias("unique_visitors")).orderBy(desc("pv_count"))
click_stats = filtered_df.filter(col("behavior_type") == "click").groupBy("click_target").agg(count("user_id").alias("click_count")).orderBy(desc("click_count"))
stay_time_stats = filtered_df.filter(col("behavior_type") == "page_view").groupBy("page_url").agg(avg("stay_duration").alias("avg_stay_time"), max("stay_duration").alias("max_stay_time"), min("stay_duration").alias("min_stay_time"))
search_keyword_stats = filtered_df.filter(col("behavior_type") == "search").groupBy("search_keyword").agg(count("user_id").alias("search_count")).orderBy(desc("search_count")).limit(20)
user_activity_stats = filtered_df.groupBy("user_id").agg(count("behavior_id").alias("total_actions"), sum(when(col("behavior_type") == "page_view", 1).otherwise(0)).alias("page_views"), sum(when(col("behavior_type") == "click", 1).otherwise(0)).alias("clicks"), sum(when(col("behavior_type") == "search", 1).otherwise(0)).alias("searches"))
bounce_rate_analysis = filtered_df.filter(col("behavior_type") == "page_view").groupBy("user_id", "session_id").agg(count("behavior_id").alias("page_count")).filter(col("page_count") == 1)
total_sessions = filtered_df.select("session_id").distinct().count()
bounce_sessions = bounce_rate_analysis.select("session_id").distinct().count()
bounce_rate = bounce_sessions / total_sessions if total_sessions > 0 else 0
conversion_funnel = filtered_df.groupBy("user_id").agg(sum(when(col("behavior_type") == "page_view", 1).otherwise(0)).alias("views"), sum(when(col("behavior_type") == "add_to_cart", 1).otherwise(0)).alias("add_carts"), sum(when(col("behavior_type") == "purchase", 1).otherwise(0)).alias("purchases"))
result_data = {"page_view_stats": [row.asDict() for row in page_view_stats.collect()[:10]], "click_stats": [row.asDict() for row in click_stats.collect()[:10]], "stay_time_stats": [row.asDict() for row in stay_time_stats.collect()[:10]], "search_keywords": [row.asDict() for row in search_keyword_stats.collect()], "user_activity": [row.asDict() for row in user_activity_stats.collect()[:20]], "bounce_rate": bounce_rate, "total_users": filtered_df.select("user_id").distinct().count(), "total_sessions": total_sessions}
return JsonResponse({"status": "success", "data": result_data})
@csrf_exempt
def user_management_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
analysis_type = data.get('analysis_type', 'overview')
user_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/travel_db").option("dbtable", "users").option("user", "root").option("password", "password").load()
behavior_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/travel_db").option("dbtable", "user_behavior").option("user", "root").option("password", "password").load()
user_behavior_joined = user_df.join(behavior_df, user_df.user_id == behavior_df.user_id, "left")
if analysis_type == 'demographic':
age_distribution = user_df.groupBy("age_group").agg(count("user_id").alias("user_count")).orderBy("age_group")
gender_distribution = user_df.groupBy("gender").agg(count("user_id").alias("user_count"))
region_distribution = user_df.groupBy("region").agg(count("user_id").alias("user_count")).orderBy(desc("user_count")).limit(10)
result_data = {"age_distribution": [row.asDict() for row in age_distribution.collect()], "gender_distribution": [row.asDict() for row in gender_distribution.collect()], "region_distribution": [row.asDict() for row in region_distribution.collect()]}
elif analysis_type == 'activity':
user_activity_levels = user_behavior_joined.groupBy("user_id", "username").agg(count("behavior_id").alias("total_activities"), count(when(col("behavior_type") == "page_view", 1)).alias("page_views"), count(when(col("behavior_type") == "click", 1)).alias("clicks"))
active_users = user_activity_levels.filter(col("total_activities") > 50).orderBy(desc("total_activities"))
inactive_users = user_activity_levels.filter(col("total_activities") < 10).orderBy("total_activities")
avg_activity = user_activity_levels.agg(avg("total_activities").alias("avg_activity")).collect()[0]["avg_activity"]
result_data = {"active_users": [row.asDict() for row in active_users.collect()[:20]], "inactive_users": [row.asDict() for row in inactive_users.collect()[:20]], "average_activity": avg_activity, "total_registered_users": user_df.count()}
user_retention_data = user_behavior_joined.groupBy("user_id").agg(min("behavior_date").alias("first_visit"), max("behavior_date").alias("last_visit"), count("behavior_id").alias("total_visits"))
retention_analysis = user_retention_data.withColumn("retention_days", (col("last_visit").cast("long") - col("first_visit").cast("long")) / 86400)
avg_retention = retention_analysis.agg(avg("retention_days").alias("avg_retention_days")).collect()[0]["avg_retention_days"]
result_data["retention_analysis"] = {"average_retention_days": avg_retention, "user_lifecycle": [row.asDict() for row in retention_analysis.orderBy(desc("retention_days")).collect()[:15]]}
return JsonResponse({"status": "success", "data": result_data})
@csrf_exempt
def data_visualization_processing(request):
if request.method == 'POST':
data = json.loads(request.body)
chart_type = data.get('chart_type')
time_range = data.get('time_range', 7)
end_date = datetime.now()
start_date = end_date - timedelta(days=time_range)
behavior_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/travel_db").option("dbtable", "user_behavior").option("user", "root").option("password", "password").load()
filtered_df = behavior_df.filter((col("behavior_date") >= start_date.strftime('%Y-%m-%d')) & (col("behavior_date") <= end_date.strftime('%Y-%m-%d')))
if chart_type == 'trend':
daily_trends = filtered_df.groupBy("behavior_date").agg(count("user_id").alias("total_behaviors"), count(when(col("behavior_type") == "page_view", 1)).alias("page_views"), count(when(col("behavior_type") == "click", 1)).alias("clicks"), count(when(col("behavior_type") == "search", 1)).alias("searches")).orderBy("behavior_date")
trend_data = [{"date": row["behavior_date"].strftime('%Y-%m-%d'), "total": row["total_behaviors"], "page_views": row["page_views"], "clicks": row["clicks"], "searches": row["searches"]} for row in daily_trends.collect()]
elif chart_type == 'heatmap':
hourly_activity = filtered_df.withColumn("hour", col("behavior_date").cast("string").substr(12, 2)).groupBy("hour").agg(count("user_id").alias("activity_count")).orderBy("hour")
heatmap_data = [{"hour": int(row["hour"]), "activity": row["activity_count"]} for row in hourly_activity.collect()]
page_popularity = filtered_df.filter(col("behavior_type") == "page_view").groupBy("page_url").agg(count("user_id").alias("visit_count")).orderBy(desc("visit_count")).limit(20)
trend_data = {"hourly_activity": heatmap_data, "page_popularity": [row.asDict() for row in page_popularity.collect()]}
elif chart_type == 'funnel':
funnel_steps = filtered_df.groupBy("user_id").agg(count(when(col("behavior_type") == "page_view", 1)).alias("views"), count(when(col("behavior_type") == "search", 1)).alias("searches"), count(when(col("behavior_type") == "click", 1)).alias("clicks"), count(when(col("behavior_type") == "add_to_cart", 1)).alias("add_carts"), count(when(col("behavior_type") == "purchase", 1)).alias("purchases"))
funnel_summary = funnel_steps.agg(count(when(col("views") > 0, 1)).alias("step1_users"), count(when(col("searches") > 0, 1)).alias("step2_users"), count(when(col("clicks") > 0, 1)).alias("step3_users"), count(when(col("add_carts") > 0, 1)).alias("step4_users"), count(when(col("purchases") > 0, 1)).alias("step5_users")).collect()[0]
trend_data = {"funnel_data": [{"step": "页面浏览", "users": funnel_summary["step1_users"]}, {"step": "搜索行为", "users": funnel_summary["step2_users"]}, {"step": "点击行为", "users": funnel_summary["step3_users"]}, {"step": "加入购物车", "users": funnel_summary["step4_users"]}, {"step": "完成购买", "users": funnel_summary["step5_users"]}]}
user_segment_analysis = filtered_df.groupBy("user_id").agg(count("behavior_id").alias("activity_count")).withColumn("user_segment", when(col("activity_count") >= 50, "高活跃用户").when(col("activity_count") >= 20, "中活跃用户").otherwise("低活跃用户"))
segment_distribution = user_segment_analysis.groupBy("user_segment").agg(count("user_id").alias("segment_count")).collect()
segment_data = [{"segment": row["user_segment"], "count": row["segment_count"]} for row in segment_distribution]
return JsonResponse({"status": "success", "chart_data": trend_data, "segment_data": segment_data, "data_summary": {"total_behaviors": filtered_df.count(), "unique_users": filtered_df.select("user_id").distinct().count(), "date_range": f"{start_date.strftime('%Y-%m-%d')} to {end_date.strftime('%Y-%m-%d')}"}})
基于大数据的旅游网站用户行为数据分析系统文档展示
💖💖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
