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一、前言

XXXX

二、开发环境

• 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

三、系统界面展示

• 基于大数据的旅游网站用户行为数据分析系统界面展示：
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  

四、部分代码设计

• 项目实战-代码参考：

from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.ml.clustering import KMeans
from pyspark.ml.feature import VectorAssembler
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from datetime import datetime, timedelta

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

def user_basic_feature_analysis(data_path):
    df = spark.read.csv(data_path, header=True, inferSchema=True)
    device_analysis = df.groupBy("preferred_device").agg(
        count("*").alias("user_count"),
        (count("*") * 100.0 / df.count()).alias("percentage"),
        sum(when(col("Buy_ticket") == "Yes", 1).otherwise(0)).alias("purchase_count"),
        (sum(when(col("Buy_ticket") == "Yes", 1).otherwise(0)) * 100.0 / count("*")).alias("conversion_rate")
    ).orderBy(desc("user_count"))
    location_analysis = df.groupBy("preferred_location_type").agg(
        count("*").alias("user_count"),
        avg("Daily_Avg_mins_spend_on_traveling_page").alias("avg_time_spent"),
        sum(when(col("Buy_ticket") == "Yes", 1).otherwise(0)).alias("purchase_count")
    )
    working_impact = df.groupBy("working_flag").agg(
        count("*").alias("total_users"),
        sum(when(col("Buy_ticket") == "Yes", 1).otherwise(0)).alias("purchasers"),
        (sum(when(col("Buy_ticket") == "Yes", 1).otherwise(0)) * 100.0 / count("*")).alias("purchase_rate"),
        avg("Yearly_avg_view_on_travel_page").alias("avg_page_views")
    )
    family_analysis = df.groupBy("member_in_family").agg(
        count("*").alias("user_count"),
        sum(when(col("Buy_ticket") == "Yes", 1).otherwise(0)).alias("buyers"),
        avg("Daily_Avg_mins_spend_on_traveling_page").alias("avg_engagement_time")
    ).orderBy("member_in_family")
    cross_analysis = df.groupBy("Adult_flag", "preferred_location_type").agg(
        count("*").alias("user_count"),
        (count("*") * 100.0 / df.count()).alias("distribution_rate")
    ).orderBy("Adult_flag", desc("user_count"))
    return {
        "device_preference": device_analysis.collect(),
        "location_preference": location_analysis.collect(),
        "working_impact": working_impact.collect(),
        "family_structure": family_analysis.collect(),
        "adult_location_cross": cross_analysis.collect()
    }

def user_interaction_behavior_analysis(data_path):
    df = spark.read.csv(data_path, header=True, inferSchema=True)
    df = df.withColumn("engagement_level", 
        when(col("Daily_Avg_mins_spend_on_traveling_page") < 5, "短时停留(0-5分钟)")
        .when(col("Daily_Avg_mins_spend_on_traveling_page") < 15, "中等停留(5-15分钟)")
        .otherwise("长时停留(>15分钟)")
    )
    engagement_analysis = df.groupBy("engagement_level").agg(
        count("*").alias("user_count"),
        avg("Yearly_avg_view_on_travel_page").alias("avg_page_views"),
        sum(when(col("Buy_ticket") == "Yes", 1).otherwise(0)).alias("conversions"),
        (sum(when(col("Buy_ticket") == "Yes", 1).otherwise(0)) * 100.0 / count("*")).alias("conversion_rate")
    ).orderBy(desc("conversion_rate"))
    comment_behavior = df.withColumn("comment_activity_level",
        when(col("Yearly_avg_comment_on_travel_page") < 10, "低评论活跃度(1-10条/月)")
        .when(col("Yearly_avg_comment_on_travel_page") < 20, "中评论活跃度(11-20条/月)")
        .otherwise("高评论活跃度(>20条/月)")
    ).groupBy("comment_activity_level").agg(
        count("*").alias("user_count"),
        avg("Daily_Avg_mins_spend_on_traveling_page").alias("avg_time_spent"),
        (sum(when(col("Buy_ticket") == "Yes", 1).otherwise(0)) * 100.0 / count("*")).alias("purchase_rate")
    )
    checkin_analysis = df.withColumn("checkin_frequency",
        when(col("yearly_avg_Outstation_checkins") == 0, "无签到")
        .when(col("yearly_avg_Outstation_checkins") < 5, "低频签到(1-4次)")
        .when(col("yearly_avg_Outstation_checkins") < 10, "中频签到(5-9次)")
        .otherwise("高频签到(≥10次)")
    ).groupBy("checkin_frequency").agg(
        count("*").alias("user_count"),
        avg("total_likes_on_outstation_checkin_given").alias("avg_likes_given"),
        (sum(when(col("Buy_ticket") == "Yes", 1).otherwise(0)) * 100.0 / count("*")).alias("conversion_rate")
    )
    social_interaction = df.withColumn("social_balance",
        col("total_likes_on_outofstation_checkin_received") - col("total_likes_on_outstation_checkin_given")
    ).select("social_balance", "Buy_ticket", "following_company_page")
    return {
        "engagement_analysis": engagement_analysis.collect(),
        "comment_behavior": comment_behavior.collect(),
        "checkin_patterns": checkin_analysis.collect(),
        "social_interaction_balance": social_interaction.collect()
    }

def user_segmentation_rfm_analysis(data_path):
    df = spark.read.csv(data_path, header=True, inferSchema=True)
    df_cleaned = df.fillna({
        "week_since_last_outstation_checkin": df.agg(avg("week_since_last_outstation_checkin")).collect()[0][0],
        "yearly_avg_Outstation_checkins": 0,
        "Daily_Avg_mins_spend_on_traveling_page": 0
    })
    recency_percentiles = df_cleaned.approxQuantile("week_since_last_outstation_checkin", [0.33, 0.67], 0.01)
    frequency_percentiles = df_cleaned.approxQuantile("yearly_avg_Outstation_checkins", [0.33, 0.67], 0.01)
    monetary_percentiles = df_cleaned.approxQuantile("Daily_Avg_mins_spend_on_traveling_page", [0.33, 0.67], 0.01)
    rfm_df = df_cleaned.withColumn("R_Score",
        when(col("week_since_last_outstation_checkin") <= recency_percentiles[0], 3)
        .when(col("week_since_last_outstation_checkin") <= recency_percentiles[1], 2)
        .otherwise(1)
    ).withColumn("F_Score",
        when(col("yearly_avg_Outstation_checkins") >= frequency_percentiles[1], 3)
        .when(col("yearly_avg_Outstation_checkins") >= frequency_percentiles[0], 2)
        .otherwise(1)
    ).withColumn("M_Score",
        when(col("Daily_Avg_mins_spend_on_traveling_page") >= monetary_percentiles[1], 3)
        .when(col("Daily_Avg_mins_spend_on_traveling_page") >= monetary_percentiles[0], 2)
        .otherwise(1)
    )
    rfm_segments = rfm_df.withColumn("RFM_Segment",
        when((col("R_Score") == 3) & (col("F_Score") == 3) & (col("M_Score") >= 2), "高价值用户")
        .when((col("R_Score") >= 2) & (col("F_Score") >= 2) & (col("M_Score") >= 2), "中高价值用户")
        .when((col("R_Score") >= 2) & (col("F_Score") == 1) & (col("M_Score") >= 2), "潜力用户")
        .when((col("R_Score") == 1) & (col("F_Score") >= 2), "流失风险用户")
        .otherwise("低价值用户")
    )
    segment_analysis = rfm_segments.groupBy("RFM_Segment").agg(
        count("*").alias("user_count"),
        avg("Yearly_avg_view_on_travel_page").alias("avg_page_views"),
        avg("total_likes_on_outstation_checkin_given").alias("avg_social_activity"),
        (sum(when(col("Buy_ticket") == "Yes", 1).otherwise(0)) * 100.0 / count("*")).alias("conversion_rate"),
        avg("R_Score").alias("avg_recency"),
        avg("F_Score").alias("avg_frequency"),
        avg("M_Score").alias("avg_monetary")
    ).orderBy(desc("conversion_rate"))
    feature_cols = ["Yearly_avg_view_on_travel_page", "yearly_avg_Outstation_checkins", "Daily_Avg_mins_spend_on_traveling_page", "total_likes_on_outstation_checkin_given"]
    assembler = VectorAssembler(inputCols=feature_cols, outputCol="features")
    feature_df = assembler.transform(rfm_df).select("features", "Buy_ticket")
    kmeans = KMeans(k=4, seed=42)
    model = kmeans.fit(feature_df)
    clustered_df = model.transform(feature_df)
    cluster_analysis = clustered_df.groupBy("prediction").agg(
        count("*").alias("cluster_size"),
        (sum(when(col("Buy_ticket") == "Yes", 1).otherwise(0)) * 100.0 / count("*")).alias("purchase_rate")
    ).orderBy("prediction")
    return {
        "rfm_segments": segment_analysis.collect(),
        "kmeans_clusters": cluster_analysis.collect(),
        "segment_distribution": rfm_segments.groupBy("RFM_Segment").count().collect()
    }

五、系统视频

• 基于大数据的旅游网站用户行为数据分析系统-项目视频：

结语

大数据毕业设计选题推荐-基于大数据的旅游网站用户行为数据分析系统-Hadoop-Spark-数据可视化-BigData
想看其他类型的计算机毕业设计作品也可以和我说~谢谢大家！
有技术这一块问题大家可以评论区交流或者私我~
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