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💖🔥作者主页：计算机毕设木哥🔥 💖
一、项目介绍
基于Python数据挖掘的高考志愿推荐系统是一个面向高中毕业生的智能化志愿填报辅助平台，通过深度整合历年高考录取数据、院校专业信息以及学生个人成绩特征，运用先进的数据挖掘算法为用户提供个性化的志愿填报建议。系统采用Django作为后端开发框架，构建稳定可靠的数据处理和业务逻辑层，前端运用Vue.js结合ElementUI组件库打造直观友好的用户交互界面，数据存储基于MySQL数据库确保信息安全性和查询效率。核心功能涵盖高校信息管理、专业信息维护、志愿推荐算法、分数预测模型等模块，管理员可以便捷地维护系统基础数据，普通用户则能够查询院校专业详情、获取智能推荐方案、了解录取概率分析等服务。系统通过对海量历史数据的深度挖掘分析，结合学生的分数段、兴趣偏好、地域要求等多维度因素，生成科学合理的志愿填报方案，有效降低志愿填报的盲目性和风险性，为广大考生提供数据驱动的决策支持。
选题背景：
随着我国高等教育规模的持续扩大和招生政策的不断完善，高考志愿填报已成为影响学生未来发展轨迹的关键环节。面对全国数千所高等院校、数万个专业方向以及复杂多变的录取规则，考生和家长在志愿填报过程中普遍存在信息获取困难、数据分析能力不足、决策依据不充分等问题。传统的志愿填报往往依赖于经验判断或简单的分数对比，缺乏科学性和系统性，容易导致高分低录或专业选择不当等情况发生。与此同时，各类志愿填报咨询服务虽然能够提供一定帮助，但往往成本较高且个性化程度有限。在大数据和人工智能技术快速发展的时代背景下，如何充分利用历年录取数据的价值，通过数据挖掘技术为考生提供更加精准、个性化的志愿填报指导，成为教育信息化领域的重要研究方向。
选题意义：
本课题的研究具有一定的理论价值和实践意义。从技术层面来看，通过将数据挖掘算法应用于高考志愿推荐场景，能够探索教育数据分析的新方法，为相关领域的研究提供参考案例。系统的开发过程有助于深化对Python数据处理、Django Web开发、前后端分离架构等技术的理解和应用。从实用角度而言，该系统能够为考生提供基于历史数据分析的志愿填报建议，在一定程度上减少信息不对称带来的决策困扰。通过整合分散的院校专业信息，建立统一的查询平台，方便用户快速获取所需信息。虽然作为毕业设计项目，系统的规模和复杂度相对有限，但其体现的数据驱动决策思路对于提升志愿填报的科学性具有一定参考价值。同时，项目的实施过程也有助于锻炼系统分析设计能力、编程实现能力以及项目管理能力，为今后从事相关技术工作奠定基础。
二、开发环境
开发语言：Python
数据库：MySQL
系统架构：B/S
后端框架：Django
前端：Vue+ElementUI
开发工具：PyCharm
三、视频展示
计算机毕设选题：基于python数据挖掘的高考志愿推荐系统
四、项目展示
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五、代码展示
from pyspark.sql import SparkSession
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
from django.shortcuts import render
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
from .models import University, Major, Student, VolunteerRecommendation
spark = SparkSession.builder.appName("GaoKaoVolunteerSystem").config("spark.executor.memory", "2g").getOrCreate()
def score_prediction_algorithm(request):
if request.method == 'POST':
data = json.loads(request.body)
student_id = data.get('student_id')
target_university = data.get('university_id')
target_major = data.get('major_id')
student = Student.objects.get(id=student_id)
current_score = student.total_score
province = student.province
year = student.graduation_year
historical_data = spark.sql(f"SELECT admission_score, year, province FROM admission_records WHERE university_id = {target_university} AND major_id = {target_major} AND province = '{province}' ORDER BY year DESC LIMIT 5")
historical_df = historical_data.toPandas()
if len(historical_df) < 3:
return JsonResponse({'success': False, 'message': '历史数据不足，无法进行预测'})
trend_analysis = historical_df['admission_score'].rolling(window=3).mean()
latest_trend = trend_analysis.iloc[-1]
score_variance = np.var(historical_df['admission_score'])
admission_probability = calculate_admission_probability(current_score, latest_trend, score_variance)
predicted_cutoff = predict_cutoff_score(historical_df, year + 1)
risk_assessment = assess_admission_risk(current_score, predicted_cutoff, score_variance)
recommendation_score = generate_recommendation_score(admission_probability, risk_assessment)
result = {
'predicted_cutoff': round(predicted_cutoff, 2),
'admission_probability': round(admission_probability * 100, 2),
'risk_level': risk_assessment,
'recommendation_score': recommendation_score,
'historical_scores': historical_df['admission_score'].tolist()
}
return JsonResponse({'success': True, 'data': result})
def intelligent_volunteer_recommendation(request):
if request.method == 'POST':
data = json.loads(request.body)
student_id = data.get('student_id')
preference_type = data.get('preference_type', 'balanced')
region_preference = data.get('region_preference', 'all')
student = Student.objects.get(id=student_id)
student_score = student.total_score
student_province = student.province
subject_type = student.subject_combination
universities_query = f"SELECT * FROM universities WHERE status = 'active'"
if region_preference != 'all':
universities_query += f" AND region = '{region_preference}'"
universities_spark_df = spark.sql(universities_query)
universities_df = universities_spark_df.toPandas()
suitable_universities = []
for index, university in universities_df.iterrows():
majors_query = f"SELECT * FROM majors WHERE university_id = {university['id']} AND subject_requirement = '{subject_type}'"
majors_df = spark.sql(majors_query).toPandas()
for major_index, major in majors_df.iterrows():
admission_records = spark.sql(f"SELECT admission_score FROM admission_records WHERE university_id = {university['id']} AND major_id = {major['id']} AND province = '{student_province}' ORDER BY year DESC LIMIT 3")
records_df = admission_records.toPandas()
if len(records_df) > 0:
avg_score = records_df['admission_score'].mean()
score_diff = student_score - avg_score
match_degree = calculate_match_degree(score_diff, university['ranking'], major['employment_rate'])
if match_degree > 0.6:
suitable_universities.append({
'university_name': university['name'],
'major_name': major['name'],
'match_degree': round(match_degree, 3),
'predicted_score': round(avg_score, 1),
'score_difference': round(score_diff, 1),
'university_ranking': university['ranking'],
'employment_rate': major['employment_rate']
})
sorted_recommendations = sorted(suitable_universities, key=lambda x: x['match_degree'], reverse=True)
final_recommendations = apply_preference_filter(sorted_recommendations, preference_type)
top_recommendations = final_recommendations[:15]
return JsonResponse({'success': True, 'recommendations': top_recommendations})
def data_mining_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
analysis_type = data.get('analysis_type', 'trend')
target_province = data.get('province', 'all')
year_range = data.get('year_range', 5)
if analysis_type == 'trend':
trend_query = f"SELECT university_id, major_id, year, AVG(admission_score) as avg_score FROM admission_records WHERE year >= {2024 - year_range} GROUP BY university_id, major_id, year ORDER BY year"
if target_province != 'all':
trend_query = trend_query.replace('WHERE', f"WHERE province = '{target_province}' AND")
trend_data = spark.sql(trend_query)
trend_df = trend_data.toPandas()
trend_analysis_result = perform_trend_analysis(trend_df)
elif analysis_type == 'correlation':
correlation_query = f"SELECT u.ranking, u.location_score, m.employment_rate, ar.admission_score FROM universities u JOIN majors m ON u.id = m.university_id JOIN admission_records ar ON u.id = ar.university_id AND m.id = ar.major_id WHERE ar.year >= {2024 - year_range}"
correlation_data = spark.sql(correlation_query)
correlation_df = correlation_data.toPandas()
correlation_matrix = correlation_df.corr()
trend_analysis_result = correlation_matrix.to_dict()
elif analysis_type == 'clustering':
clustering_query = f"SELECT university_id, AVG(admission_score) as avg_score, COUNT(*) as record_count FROM admission_records WHERE year >= {2024 - year_range} GROUP BY university_id HAVING record_count >= 10"
clustering_data = spark.sql(clustering_query)
clustering_df = clustering_data.toPandas()
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=5, random_state=42)
clusters = kmeans.fit_predict(clustering_df[['avg_score']])
clustering_df['cluster'] = clusters
trend_analysis_result = clustering_df.groupby('cluster').agg({'avg_score': ['mean', 'count']}).to_dict()
mining_insights = generate_mining_insights(trend_analysis_result, analysis_type)
actionable_suggestions = create_actionable_suggestions(mining_insights, analysis_type)
return JsonResponse({
'success': True,
'analysis_results': trend_analysis_result,
'insights': mining_insights,
'suggestions': actionable_suggestions
})
def calculate_admission_probability(student_score, predicted_cutoff, score_variance):
score_diff = student_score - predicted_cutoff
normalized_diff = score_diff / np.sqrt(score_variance)
probability = 1 / (1 + np.exp(-normalized_diff * 0.1))
return min(max(probability, 0.05), 0.95)
def predict_cutoff_score(historical_df, target_year):
years = historical_df.index.values.reshape(-1, 1)
scores = historical_df['admission_score'].values
model = RandomForestRegressor(n_estimators=50, random_state=42)
model.fit(years, scores)
predicted_score = model.predict([[target_year]])[0]
return predicted_score
def assess_admission_risk(student_score, predicted_cutoff, variance):
risk_threshold = predicted_cutoff + np.sqrt(variance)
if student_score >= risk_threshold:
return 'low'
elif student_score >= predicted_cutoff:
return 'medium'
else:
return 'high'
def calculate_match_degree(score_diff, university_ranking, employment_rate):
score_factor = 1 / (1 + np.exp(-score_diff * 0.01))
ranking_factor = (1000 - university_ranking) / 1000
employment_factor = employment_rate / 100
match_degree = (score_factor * 0.5 + ranking_factor * 0.3 + employment_factor * 0.2)
return match_degree
def apply_preference_filter(recommendations, preference_type):
if preference_type == 'score_priority':
return sorted(recommendations, key=lambda x: x['score_difference'], reverse=True)
elif preference_type == 'ranking_priority':
return sorted(recommendations, key=lambda x: x['university_ranking'])
elif preference_type == 'employment_priority':
return sorted(recommendations, key=lambda x: x['employment_rate'], reverse=True)
else:
return recommendations
def perform_trend_analysis(trend_df):
trend_results = {}
for university_major in trend_df.groupby(['university_id', 'major_id']):
group_data = university_major[1]
if len(group_data) >= 3:
slope = np.polyfit(group_data['year'], group_data['avg_score'], 1)[0]
trend_results[f"{university_major[0][0]}_{university_major[0][1]}"] = slope
return trend_results
def generate_mining_insights(analysis_results, analysis_type):
insights = []
if analysis_type == 'trend':
increasing_trends = [k for k, v in analysis_results.items() if v > 2]
decreasing_trends = [k for k, v in analysis_results.items() if v < -2]
insights.append(f"发现{len(increasing_trends)}个专业录取分数呈上升趋势")
insights.append(f"发现{len(decreasing_trends)}个专业录取分数呈下降趋势")
return insights
def create_actionable_suggestions(insights, analysis_type):
suggestions = []
if analysis_type == 'trend':
suggestions.append("关注录取分数下降的专业，可能存在报考机会")
suggestions.append("谨慎选择录取分数快速上升的热门专业")
return suggestions
def generate_recommendation_score(probability, risk_level):
base_score = probability * 100
if risk_level == 'low':
return min(base_score + 10, 95)
elif risk_level == 'high':
return max(base_score - 15, 5)
return base_score
六、项目文档展示
七、总结
本课题通过设计和实现基于Python数据挖掘的高考志愿推荐系统，成功将现代数据分析技术应用到教育信息化服务领域。项目采用Django框架构建后端服务架构，结合Vue.js和ElementUI打造用户友好的前端界面，通过MySQL数据库确保数据存储的稳定性和查询效率。系统核心在于运用数据挖掘算法对历年高考录取数据进行深度分析，建立分数预测模型和志愿推荐算法，为考生提供个性化的填报建议。通过整合Spark大数据处理引擎和机器学习算法，实现了对海量教育数据的高效处理和智能分析。项目的技术实现涵盖了数据预处理、特征工程、模型训练、结果评估等完整的数据挖掘流程，同时兼顾了系统的实用性和可扩展性。虽然作为毕业设计项目在数据规模和算法复杂度方面存在一定局限性，但该系统展现了数据驱动决策在教育服务领域的应用潜力，为解决高考志愿填报信息不对称问题提供了技术思路。项目的完成不仅锻炼了全栈开发能力和数据分析技能，也为今后从事相关技术工作积累了宝贵的实践经验。
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💖🔥作者主页：计算机毕设木哥🔥 💖