💖💖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
搭建网站框架音乐系统介绍
网站框架音乐系统是一个集成大数据技术的综合性音乐平台，采用Hadoop+Spark大数据框架作为核心技术架构，支持Python+Django和Java+SpringBoot双版本开发模式。系统前端采用Vue+ElementUI构建现代化用户界面，结合Echarts实现数据可视化展示，后端通过MySQL数据库存储音乐信息、用户数据及播放记录。系统具备完整的音乐生态功能，包括系统首页展示、用户注册登录管理、歌曲分类与标签体系、在线试听与音乐购买、个性化音乐播放器、我的歌单管理、详细音乐信息展示、在线留言互动、系统后台管理以及个人中心设置等十二大核心模块。通过Hadoop分布式存储处理大量音乐文件，利用Spark进行实时数据分析和推荐算法计算，结合Pandas和NumPy进行数据预处理，为用户提供智能化的音乐服务体验，实现了传统音乐网站向大数据驱动音乐平台的技术升级。
搭建网站框架音乐系统演示视频
搭建网站框架音乐系统演示图片
搭建网站框架音乐系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, avg, desc, asc
from django.http import JsonResponse
from django.shortcuts import get_object_or_404
from django.db import transaction
from django.core.paginator import Paginator
from django.contrib.auth.decorators import login_required
import pandas as pd
import numpy as np
from models import Music, User, Playlist, PlayRecord
spark = SparkSession.builder.appName("MusicDataAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
@login_required
def music_play_service(request, music_id):
try:
music = get_object_or_404(Music, id=music_id)
user = request.user
with transaction.atomic():
music.play_count += 1
music.save()
play_record = PlayRecord.objects.create(
user=user,
music=music,
play_duration=0,
device_type=request.META.get('HTTP_USER_AGENT', 'unknown')
)
play_data = PlayRecord.objects.filter(music=music).values(
'user_id', 'play_duration', 'created_at'
)
df_play = pd.DataFrame(list(play_data))
if not df_play.empty:
spark_df = spark.createDataFrame(df_play)
music_stats = spark_df.groupBy().agg(
count("user_id").alias("total_plays"),
avg("play_duration").alias("avg_duration")
).collect()[0]
music.avg_play_duration = float(music_stats['avg_duration']) if music_stats['avg_duration'] else 0
music.total_unique_plays = spark_df.select("user_id").distinct().count()
music.save()
similar_users = PlayRecord.objects.filter(music=music).exclude(user=user).values_list('user_id', flat=True)[:10]
recommendations = []
if similar_users:
similar_music = PlayRecord.objects.filter(
user_id__in=similar_users
).exclude(music=music).values('music__id', 'music__title').annotate(
play_frequency=count('music_id')
).order_by('-play_frequency')[:5]
recommendations = list(similar_music)
return JsonResponse({
'status': 'success',
'music_info': {
'id': music.id,
'title': music.title,
'artist': music.artist,
'file_url': music.file_url,
'play_count': music.play_count,
'duration': music.duration
},
'recommendations': recommendations,
'play_record_id': play_record.id
})
except Exception as e:
return JsonResponse({'status': 'error', 'message': str(e)})
def playlist_management_service(request):
try:
user = request.user
action = request.POST.get('action')
if action == 'create':
playlist_name = request.POST.get('playlist_name')
description = request.POST.get('description', '')
with transaction.atomic():
playlist = Playlist.objects.create(
name=playlist_name,
user=user,
description=description,
is_public=request.POST.get('is_public', False)
)
user_playlists = Playlist.objects.filter(user=user).values(
'id', 'name', 'created_at', 'music_count'
)
df_playlists = pd.DataFrame(list(user_playlists))
if not df_playlists.empty:
spark_df = spark.createDataFrame(df_playlists)
playlist_stats = spark_df.groupBy().agg(
count("id").alias("total_playlists"),
avg("music_count").alias("avg_music_per_playlist")
).collect()[0]
user.total_playlists = int(playlist_stats['total_playlists'])
user.avg_playlist_size = float(playlist_stats['avg_music_per_playlist']) if playlist_stats['avg_music_per_playlist'] else 0
user.save()
elif action == 'add_music':
playlist_id = request.POST.get('playlist_id')
music_id = request.POST.get('music_id')
playlist = get_object_or_404(Playlist, id=playlist_id, user=user)
music = get_object_or_404(Music, id=music_id)
if not playlist.music_items.filter(id=music_id).exists():
playlist.music_items.add(music)
playlist.music_count += 1
playlist.save()
playlist_music = playlist.music_items.values('category', 'artist', 'duration')
df_music = pd.DataFrame(list(playlist_music))
if not df_music.empty:
spark_df = spark.createDataFrame(df_music)
category_stats = spark_df.groupBy("category").agg(
count("category").alias("count")
).orderBy(desc("count")).collect()
playlist.dominant_category = category_stats[0]['category'] if category_stats else 'Unknown'
playlist.total_duration = spark_df.agg({"duration": "sum"}).collect()[0][0]
playlist.save()
user_playlists = Playlist.objects.filter(user=user).order_by('-created_at')
paginator = Paginator(user_playlists, 10)
page_number = request.GET.get('page', 1)
page_playlists = paginator.get_page(page_number)
return JsonResponse({
'status': 'success',
'playlists': [{
'id': p.id,
'name': p.name,
'music_count': p.music_count,
'total_duration': p.total_duration,
'dominant_category': p.dominant_category,
'created_at': p.created_at.strftime('%Y-%m-%d')
} for p in page_playlists],
'pagination': {
'current_page': page_playlists.number,
'total_pages': paginator.num_pages,
'has_next': page_playlists.has_next(),
'has_previous': page_playlists.has_previous()
}
})
except Exception as e:
return JsonResponse({'status': 'error', 'message': str(e)})
def music_recommendation_service(request):
try:
user = request.user
user_play_records = PlayRecord.objects.filter(user=user).values(
'music__id', 'music__category', 'music__artist', 'play_duration', 'music__title'
)
df_user_history = pd.DataFrame(list(user_play_records))
recommendations = []
if not df_user_history.empty:
spark_df = spark.createDataFrame(df_user_history)
category_preferences = spark_df.groupBy("music__category").agg(
count("music__id").alias("play_count"),
avg("play_duration").alias("avg_duration")
).orderBy(desc("play_count")).collect()
preferred_categories = [row['music__category'] for row in category_preferences[:3]]
artist_preferences = spark_df.groupBy("music__artist").agg(
count("music__id").alias("play_count")
).orderBy(desc("play_count")).collect()
preferred_artists = [row['music__artist'] for row in artist_preferences[:5]]
played_music_ids = df_user_history['music__id'].unique().tolist()
candidate_music = Music.objects.filter(
category__in=preferred_categories
).exclude(id__in=played_music_ids).values(
'id', 'title', 'artist', 'category', 'play_count', 'rating'
)
df_candidates = pd.DataFrame(list(candidate_music))
if not df_candidates.empty:
spark_candidates = spark.createDataFrame(df_candidates)
weighted_scores = spark_candidates.withColumn(
"preference_score",
col("play_count") * 0.4 + col("rating") * 0.6
).orderBy(desc("preference_score"))
top_recommendations = weighted_scores.limit(10).collect()
recommendations = [{
'music_id': row['id'],
'title': row['title'],
'artist': row['artist'],
'category': row['category'],
'score': float(row['preference_score']),
'reason': f"基于您对{row['category']}类音乐的喜好推荐"
} for row in top_recommendations]
collaborative_users = PlayRecord.objects.filter(
music__id__in=played_music_ids
).exclude(user=user).values('user_id').annotate(
common_count=count('music_id')
).filter(common_count__gte=2).order_by('-common_count')[:5]
if collaborative_users:
similar_user_ids = [u['user_id'] for u in collaborative_users]
collaborative_music = PlayRecord.objects.filter(
user_id__in=similar_user_ids
).exclude(music_id__in=played_music_ids).values(
'music__id', 'music__title', 'music__artist'
).annotate(play_frequency=count('music_id')).order_by('-play_frequency')[:5]
collab_recommendations = [{
'music_id': item['music__id'],
'title': item['music__title'],
'artist': item['music__artist'],
'score': item['play_frequency'],
'reason': "相似用户也喜欢的音乐"
} for item in collaborative_music]
recommendations.extend(collab_recommendations)
if not recommendations:
popular_music = Music.objects.order_by('-play_count')[:10].values(
'id', 'title', 'artist', 'category', 'play_count'
)
recommendations = [{
'music_id': music['id'],
'title': music['title'],
'artist': music['artist'],
'category': music['category'],
'score': music['play_count'],
'reason': "热门音乐推荐"
} for music in popular_music]
return JsonResponse({
'status': 'success',
'recommendations': recommendations[:15],
'user_preferences': {
'preferred_categories': preferred_categories if 'preferred_categories' in locals() else [],
'preferred_artists': preferred_artists if 'preferred_artists' in locals() else []
}
})
except Exception as e:
return JsonResponse({'status': 'error', 'message': str(e)})
搭建网站框架音乐系统文档展示
💖💖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
