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旅游网站用户行为数据分析系统 - 简介
基于Hadoop+Django的旅游网站用户行为数据分析系统是一套完整的大数据分析解决方案，专门针对旅游网站的用户行为数据进行深度挖掘和分析。系统采用Hadoop分布式存储架构，结合Spark大数据处理引擎，能够高效处理海量的用户行为数据。后端基于Django框架构建，前端采用Vue+ElementUI+ECharts技术栈，为用户提供直观的数据可视化界面。系统核心功能包括用户基础特征分析、用户互动行为分析、用户社交网络影响分析和用户分群与行为模式分析四大模块。通过对用户设备偏好、旅游地点偏好、页面浏览深度、社交互动活跃度等多维度数据的分析，系统能够帮助旅游网站深入了解用户行为模式，发现高价值用户特征，为精准营销和个性化推荐提供数据支撑。系统还实现了RFM用户价值分群、K-means聚类分析等高级数据挖掘功能，能够自动识别不同类型用户群体的行为特征，为旅游网站的运营决策提供科学依据。整个系统架构清晰，技术栈完整，具有良好的扩展性和实用性。
旅游网站用户行为数据分析系统 -技术
开发语言：java或Python
数据库：MySQL
系统架构：B/S
前端：Vue+ElementUI+HTML+CSS+JavaScript+jQuery+Echarts
大数据框架：Hadoop+Spark（本次没用Hive，支持定制）
后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)
旅游网站用户行为数据分析系统 - 背景
随着互联网技术的快速发展和人民生活水平的提升，在线旅游市场呈现出蓬勃发展的态势。越来越多的用户选择通过旅游网站来规划和预订自己的出行，这些网站每天都会产生大量的用户行为数据，包括用户的浏览记录、搜索偏好、互动行为、购买决策等多维度信息。然而，传统的数据分析方法往往局限于简单的统计汇总，难以应对海量数据的处理需求，也无法深入挖掘用户行为背后的规律和价值。面对日益激烈的市场竞争，旅游网站迫切需要通过大数据技术来分析用户行为，了解用户真实需求，提升用户体验和转化率。目前市场上缺乏专门针对旅游领域的用户行为分析系统，大多数解决方案要么过于通用化，要么技术门槛过高，难以满足中小型旅游网站的实际需求。
本系统的开发具有重要的理论价值和实际应用意义。从技术角度来看，系统将Hadoop分布式存储与Spark大数据处理技术相结合，为处理旅游网站海量用户数据提供了可行的技术方案，能够有效解决传统单机处理能力不足的问题。从商业应用角度分析，系统通过多维度的用户行为分析，能够帮助旅游网站更好地理解用户需求，识别高价值客户群体，为制定精准的营销策略和个性化推荐算法提供数据支撑，从而提升网站的用户留存率和转化率。从学术研究层面看，本系统为旅游领域的用户行为分析提供了具体的实现方案和参考模型，丰富了该领域的研究成果。同时，系统采用的RFM模型、聚类分析等数据挖掘方法在旅游行业的应用实践，也为相关理论研究提供了有价值的案例。虽然作为毕业设计项目，系统规模相对有限，但其设计思路和技术实现对于同类型系统的开发仍具有一定的参考价值。
旅游网站用户行为数据分析系统 -视频展示
旅游网站用户行为数据分析系统 -图片展示
旅游网站用户行为数据分析系统 -代码展示
from pyspark.sql.functions import col, when, count, avg, sum as spark_sum, desc, asc
from pyspark.ml.feature import VectorAssembler, StandardScaler
from pyspark.ml.clustering import KMeans
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("TravelUserAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def user_device_preference_analysis(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/travel_db").option("dbtable", "user_behavior").option("user", "root").option("password", "password").load()
device_stats = df.groupBy("preferred_device").agg(count("*").alias("user_count"), (count("*") * 100.0 / df.count()).alias("percentage")).orderBy(desc("user_count"))
device_purchase_rate = df.groupBy("preferred_device").agg(count("*").alias("total_users"), spark_sum(when(col("Buy_ticket") == "Yes", 1).otherwise(0)).alias("purchase_users")).withColumn("purchase_rate", col("purchase_users") * 100.0 / col("total_users")).orderBy(desc("purchase_rate"))
location_device_cross = df.groupBy("preferred_device", "preferred_location_type").agg(count("*").alias("count")).orderBy("preferred_device", desc("count"))
working_device_relation = df.groupBy("preferred_device", "working_flag").agg(count("*").alias("count"), avg("Daily_Avg_mins_spend_on_traveling_page").alias("avg_time_spent")).orderBy("preferred_device", "working_flag")
family_device_impact = df.groupBy("preferred_device").agg(avg("member_in_family").alias("avg_family_size"), spark_sum(when(col("Buy_ticket") == "Yes", 1).otherwise(0)).alias("purchase_count"), count("*").alias("total_count")).withColumn("family_purchase_rate", col("purchase_count") * 100.0 / col("total_count")).orderBy(desc("avg_family_size"))
device_engagement = df.groupBy("preferred_device").agg(avg("Yearly_avg_view_on_travel_page").alias("avg_page_views"), avg("Yearly_avg_comment_on_travel_page").alias("avg_comments"), avg("total_likes_on_outstation_checkin_given").alias("avg_likes_given")).orderBy(desc("avg_page_views"))
adult_device_behavior = df.groupBy("preferred_device", "Adult_flag").agg(count("*").alias("user_count"), avg("travelling_network_rating").alias("avg_network_rating"), spark_sum(when(col("Buy_ticket") == "Yes", 1).otherwise(0)).alias("purchases")).withColumn("purchase_rate", col("purchases") * 100.0 / col("user_count")).orderBy("preferred_device", "Adult_flag")
device_social_activity = df.groupBy("preferred_device").agg(avg("total_likes_on_outofstation_checkin_received").alias("avg_likes_received"), avg("yearly_avg_Outstation_checkins").alias("avg_checkins"), avg("week_since_last_outstation_checkin").alias("avg_weeks_since_checkin")).orderBy(desc("avg_social_activity"))
result_data = {"device_stats": device_stats.collect(), "device_purchase_rate": device_purchase_rate.collect(), "location_device_cross": location_device_cross.collect(), "working_device_relation": working_device_relation.collect(), "family_device_impact": family_device_impact.collect(), "device_engagement": device_engagement.collect(), "adult_device_behavior": adult_device_behavior.collect(), "device_social_activity": device_social_activity.collect()}
processed_result = {}
for key, value in result_data.items():
processed_result[key] = [row.asDict() for row in value]
return JsonResponse({"status": "success", "data": processed_result, "message": "用户设备偏好分析完成"})
def user_interaction_behavior_analysis(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/travel_db").option("dbtable", "user_behavior").option("user", "root").option("password", "password").load()
view_depth_segments = df.withColumn("view_segment", when(col("Yearly_avg_view_on_travel_page") < 10, "低频浏览").when(col("Yearly_avg_view_on_travel_page") < 50, "中频浏览").otherwise("高频浏览"))
view_conversion = view_depth_segments.groupBy("view_segment").agg(count("*").alias("total_users"), spark_sum(when(col("Buy_ticket") == "Yes", 1).otherwise(0)).alias("converted_users")).withColumn("conversion_rate", col("converted_users") * 100.0 / col("total_users")).orderBy(desc("conversion_rate"))
time_value_analysis = df.withColumn("time_segment", when(col("Daily_Avg_mins_spend_on_traveling_page") < 30, "短时停留").when(col("Daily_Avg_mins_spend_on_traveling_page") < 60, "中时停留").otherwise("长时停留")).groupBy("time_segment").agg(count("*").alias("user_count"), spark_sum(when(col("Buy_ticket") == "Yes", 1).otherwise(0)).alias("purchases"), avg("Daily_Avg_mins_spend_on_traveling_page").alias("avg_time")).withColumn("time_conversion_rate", col("purchases") * 100.0 / col("user_count")).withColumn("value_per_minute", col("purchases") / col("avg_time")).orderBy(desc("value_per_minute"))
social_activity_score = df.withColumn("social_score", col("total_likes_on_outstation_checkin_given") + col("Yearly_avg_comment_on_travel_page") * 2).withColumn("social_level", when(col("social_score") < 10, "低活跃").when(col("social_score") < 50, "中活跃").otherwise("高活跃"))
social_conversion = social_activity_score.groupBy("social_level").agg(count("*").alias("total_users"), spark_sum(when(col("Buy_ticket") == "Yes", 1).otherwise(0)).alias("converted_users"), avg("social_score").alias("avg_social_score")).withColumn("social_conversion_rate", col("converted_users") * 100.0 / col("total_users")).orderBy(desc("social_conversion_rate"))
checkin_behavior_analysis = df.withColumn("checkin_frequency", when(col("yearly_avg_Outstation_checkins") < 5, "低频签到").when(col("yearly_avg_Outstation_checkins") < 15, "中频签到").otherwise("高频签到")).withColumn("recent_checkin", when(col("week_since_last_outstation_checkin") < 4, "近期活跃").when(col("week_since_last_outstation_checkin") < 12, "一般活跃").otherwise("不活跃"))
checkin_purchase_relation = checkin_behavior_analysis.groupBy("checkin_frequency", "recent_checkin").agg(count("*").alias("user_count"), spark_sum(when(col("Buy_ticket") == "Yes", 1).otherwise(0)).alias("purchases"), avg("yearly_avg_Outstation_checkins").alias("avg_checkins")).withColumn("checkin_conversion_rate", col("purchases") * 100.0 / col("user_count")).orderBy(desc("checkin_conversion_rate"))
comment_engagement_impact = df.withColumn("comment_level", when(col("montly_avg_comment_on_company_page") < 2, "低评论").when(col("montly_avg_comment_on_company_page") < 5, "中评论").otherwise("高评论")).groupBy("comment_level").agg(count("*").alias("total_users"), spark_sum(when(col("Buy_ticket") == "Yes", 1).otherwise(0)).alias("converted_users"), avg("montly_avg_comment_on_company_page").alias("avg_comments")).withColumn("comment_conversion_rate", col("converted_users") * 100.0 / col("total_users")).orderBy(desc("comment_conversion_rate"))
interaction_correlation = df.select("Yearly_avg_view_on_travel_page", "Daily_Avg_mins_spend_on_traveling_page", "Yearly_avg_comment_on_travel_page", "total_likes_on_outstation_checkin_given", "yearly_avg_Outstation_checkins").toPandas().corr()
result_data = {"view_conversion": view_conversion.collect(), "time_value_analysis": time_value_analysis.collect(), "social_conversion": social_conversion.collect(), "checkin_purchase_relation": checkin_purchase_relation.collect(), "comment_engagement_impact": comment_engagement_impact.collect(), "interaction_correlation": interaction_correlation.to_dict()}
processed_result = {}
for key, value in result_data.items():
if key != "interaction_correlation":
processed_result[key] = [row.asDict() for row in value]
else:
processed_result[key] = value
return JsonResponse({"status": "success", "data": processed_result, "message": "用户互动行为分析完成"})
def user_clustering_analysis(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/travel_db").option("dbtable", "user_behavior").option("user", "root").option("password", "password").load()
feature_columns = ["Yearly_avg_view_on_travel_page", "Daily_Avg_mins_spend_on_traveling_page", "Yearly_avg_comment_on_travel_page", "total_likes_on_outstation_checkin_given", "yearly_avg_Outstation_checkins", "travelling_network_rating", "member_in_family"]
df_features = df.select(feature_columns + ["Buy_ticket"]).na.drop()
assembler = VectorAssembler(inputCols=feature_columns, outputCol="features")
feature_df = assembler.transform(df_features)
scaler = StandardScaler(inputCol="features", outputCol="scaledFeatures", withStd=True, withMean=False)
scaler_model = scaler.fit(feature_df)
scaled_df = scaler_model.transform(feature_df)
kmeans = KMeans(featuresCol="scaledFeatures", predictionCol="cluster", k=5, seed=42, maxIter=100)
kmeans_model = kmeans.fit(scaled_df)
clustered_df = kmeans_model.transform(scaled_df)
cluster_analysis = clustered_df.groupBy("cluster").agg(count("*").alias("cluster_size"), spark_sum(when(col("Buy_ticket") == "Yes", 1).otherwise(0)).alias("purchases"), avg("Yearly_avg_view_on_travel_page").alias("avg_page_views"), avg("Daily_Avg_mins_spend_on_traveling_page").alias("avg_time_spent"), avg("Yearly_avg_comment_on_travel_page").alias("avg_comments"), avg("total_likes_on_outstation_checkin_given").alias("avg_likes_given"), avg("yearly_avg_Outstation_checkins").alias("avg_checkins"), avg("travelling_network_rating").alias("avg_network_rating"), avg("member_in_family").alias("avg_family_size")).withColumn("purchase_rate", col("purchases") * 100.0 / col("cluster_size")).orderBy("cluster")
high_value_users = clustered_df.filter(col("Buy_ticket") == "Yes").groupBy("cluster").agg(count("*").alias("high_value_count"), avg("Yearly_avg_view_on_travel_page").alias("hv_avg_views"), avg("Daily_Avg_mins_spend_on_traveling_page").alias("hv_avg_time"), avg("travelling_network_rating").alias("hv_avg_rating")).orderBy(desc("high_value_count"))
rfm_analysis = df.withColumn("recency_score", when(col("week_since_last_outstation_checkin") < 4, 5).when(col("week_since_last_outstation_checkin") < 8, 4).when(col("week_since_last_outstation_checkin") < 12, 3).when(col("week_since_last_outstation_checkin") < 24, 2).otherwise(1)).withColumn("frequency_score", when(col("yearly_avg_Outstation_checkins") >= 20, 5).when(col("yearly_avg_Outstation_checkins") >= 15, 4).when(col("yearly_avg_Outstation_checkins") >= 10, 3).when(col("yearly_avg_Outstation_checkins") >= 5, 2).otherwise(1)).withColumn("monetary_score", when(col("Daily_Avg_mins_spend_on_traveling_page") >= 90, 5).when(col("Daily_Avg_mins_spend_on_traveling_page") >= 60, 4).when(col("Daily_Avg_mins_spend_on_traveling_page") >= 30, 3).when(col("Daily_Avg_mins_spend_on_traveling_page") >= 15, 2).otherwise(1))
rfm_segments = rfm_analysis.withColumn("rfm_score", col("recency_score") + col("frequency_score") + col("monetary_score")).withColumn("customer_segment", when(col("rfm_score") >= 12, "冠军客户").when(col("rfm_score") >= 9, "忠诚客户").when(col("rfm_score") >= 6, "潜力客户").otherwise("流失风险客户"))
rfm_segment_analysis = rfm_segments.groupBy("customer_segment").agg(count("*").alias("segment_size"), spark_sum(when(col("Buy_ticket") == "Yes", 1).otherwise(0)).alias("purchases"), avg("rfm_score").alias("avg_rfm_score"), avg("recency_score").alias("avg_recency"), avg("frequency_score").alias("avg_frequency"), avg("monetary_score").alias("avg_monetary")).withColumn("segment_conversion_rate", col("purchases") * 100.0 / col("segment_size")).orderBy(desc("avg_rfm_score"))
behavior_pattern_analysis = clustered_df.groupBy("cluster").agg(avg("total_likes_on_outofstation_checkin_received").alias("avg_likes_received"), avg("montly_avg_comment_on_company_page").alias("avg_company_comments"), spark_sum(when(col("following_company_page") == "Yes", 1).otherwise(0)).alias("following_count"), count("*").alias("total_in_cluster")).withColumn("following_rate", col("following_count") * 100.0 / col("total_in_cluster")).orderBy("cluster")
result_data = {"cluster_analysis": cluster_analysis.collect(), "high_value_users": high_value_users.collect(), "rfm_segment_analysis": rfm_segment_analysis.collect(), "behavior_pattern_analysis": behavior_pattern_analysis.collect()}
processed_result = {}
for key, value in result_data.items():
processed_result[key] = [row.asDict() for row in value]
cluster_centers = kmeans_model.clusterCenters()
processed_result["cluster_centers"] = [center.toArray().tolist() for center in cluster_centers]
return JsonResponse({"status": "success", "data": processed_result, "message": "用户聚类分析完成"})
旅游网站用户行为数据分析系统 -结语
2026届毕业生必看：不懂Hadoop大数据分析的旅游网站系统已经过时了
为什么基于Hadoop+Django的旅游网站用户行为数据分析系统成为毕设首选？
100万+数据处理：基于Hadoop集群的旅游网站用户行为分析系统大数据处理实战
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