精彩專欄推薦訂閱：在 下方專欄👇🏻👇🏻👇🏻👇🏻

💖🔥作者主頁：計算機畢設木哥🔥 💖

一、項目介紹

基于Python數據挖掘的高考志愿推薦系統是一個面向高中畢業生的智能化志愿填報輔助平臺，通過深度整合歷年高考錄取數據、院校專業信息以及學生個人成績特征，運用先進的數據挖掘算法為用戶提供個性化的志愿填報建議。系統采用Django作為后端開發框架，構建穩定可靠的數據處理和業務邏輯層，前端運用Vue.js結合ElementUI組件庫打造直觀友好的用戶交互界面，數據存儲基于MySQL數據庫確保信息安全性和查詢效率。核心功能涵蓋高校信息管理、專業信息維護、志愿推薦算法、分數預測模型等模塊，管理員可以便捷地維護系統基礎數據，普通用戶則能夠查詢院校專業詳情、獲取智能推薦方案、了解錄取概率分析等服務。系統通過對海量歷史數據的深度挖掘分析，結合學生的分數段、興趣偏好、地域要求等多維度因素，生成科學合理的志愿填報方案，有效降低志愿填報的盲目性和風險性，為廣大考生提供數據驅動的決策支持。

選題背景：

隨著我國高等教育規模的持續擴大和招生政策的不斷完善，高考志愿填報已成為影響學生未來發展軌跡的關鍵環節。面對全國數千所高等院校、數萬個專業方向以及復雜多變的錄取規則，考生和家長在志愿填報過程中普遍存在信息獲取困難、數據分析能力不足、決策依據不充分等問題。傳統的志愿填報往往依賴于經驗判斷或簡單的分數對比，缺乏科學性和系統性，容易導致高分低錄或專業選擇不當等情況發生。與此同時，各類志愿填報咨詢服務雖然能夠提供一定幫助，但往往成本較高且個性化程度有限。在大數據和人工智能技術快速發展的時代背景下，如何充分利用歷年錄取數據的價值，通過數據挖掘技術為考生提供更加精準、個性化的志愿填報指導，成為教育信息化領域的重要研究方向。

選題意義：

本課題的研究具有一定的理論價值和實踐意義。從技術層面來看，通過將數據挖掘算法應用于高考志愿推薦場景，能夠探索教育數據分析的新方法，為相關領域的研究提供參考案例。系統的開發過程有助于深化對Python數據處理、Django Web開發、前后端分離架構等技術的理解和應用。從實用角度而言，該系統能夠為考生提供基于歷史數據分析的志愿填報建議，在一定程度上減少信息不對稱帶來的決策困擾。通過整合分散的院校專業信息，建立統一的查詢平臺，方便用戶快速獲取所需信息。雖然作為畢業設計項目，系統的規模和復雜度相對有限，但其體現的數據驅動決策思路對于提升志愿填報的科學性具有一定參考價值。同時，項目的實施過程也有助于鍛煉系統分析設計能力、編程實現能力以及項目管理能力，為今后從事相關技術工作奠定基礎。

二、開發環境

開發語言：Python
數據庫：MySQL
系統架構：B/S
后端框架：Django
前端：Vue+ElementUI
開發工具：PyCharm

三、視頻展示

四、項目展示

登錄模塊：

首頁模塊：

管理模塊：

五、代碼展示

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_scoreprovince = student.provinceyear = student.graduation_yearhistorical_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': '歷史數據不足，無法進行預測'})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_scorestudent_province = student.provincesubject_type = student.subject_combinationuniversities_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_scorematch_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 KMeanskmeans = KMeans(n_clusters=5, random_state=42)clusters = kmeans.fit_predict(clustering_df[['avg_score']])clustering_df['cluster'] = clusterstrend_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_cutoffnormalized_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'].valuesmodel = 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) / 1000employment_factor = employment_rate / 100match_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]}"] = slopereturn 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"發現{len(increasing_trends)}個專業錄取分數呈上升趨勢")insights.append(f"發現{len(decreasing_trends)}個專業錄取分數呈下降趨勢")return insights
def create_actionable_suggestions(insights, analysis_type):suggestions = []if analysis_type == 'trend':suggestions.append("關注錄取分數下降的專業，可能存在報考機會")suggestions.append("謹慎選擇錄取分數快速上升的熱門專業")return suggestions
def generate_recommendation_score(probability, risk_level):base_score = probability * 100if risk_level == 'low':return min(base_score + 10, 95)elif risk_level == 'high':return max(base_score - 15, 5)return base_score

六、項目文檔展示

七、總結

本課題通過設計和實現基于Python數據挖掘的高考志愿推薦系統，成功將現代數據分析技術應用到教育信息化服務領域。項目采用Django框架構建后端服務架構，結合Vue.js和ElementUI打造用戶友好的前端界面，通過MySQL數據庫確保數據存儲的穩定性和查詢效率。系統核心在于運用數據挖掘算法對歷年高考錄取數據進行深度分析，建立分數預測模型和志愿推薦算法，為考生提供個性化的填報建議。通過整合Spark大數據處理引擎和機器學習算法，實現了對海量教育數據的高效處理和智能分析。項目的技術實現涵蓋了數據預處理、特征工程、模型訓練、結果評估等完整的數據挖掘流程，同時兼顧了系統的實用性和可擴展性。雖然作為畢業設計項目在數據規模和算法復雜度方面存在一定局限性，但該系統展現了數據驅動決策在教育服務領域的應用潛力，為解決高考志愿填報信息不對稱問題提供了技術思路。項目的完成不僅鍛煉了全棧開發能力和數據分析技能，也為今后從事相關技術工作積累了寶貴的實踐經驗。

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💖🔥作者主頁：計算機畢設木哥🔥 💖