一、个人简介
💖💖作者：计算机编程果茶熊 💙💙个人简介：曾长期从事计算机专业培训教学，担任过编程老师，同时本人也热爱上课教学，擅长Java、微信小程序、Python、Golang、安卓Android等多个IT方向。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 计算机毕业设计选题 💕💕文末获取源码联系计算机编程果茶熊
二、系统介绍
开发语言：Java+Python 数据库：MySQL 系统架构：B/S 后端框架：SpringBoot(Spring+SpringMVC+Mybatis)+Django 前端：Vue+HTML+CSS+JavaScript+jQuery
毕业生就业智能推荐信息系统采用Python+Django作为后端开发框，Vue+ElementUI构建前端交互界面，MySQL作为数据存储引擎，形成了完整的B/S架构Web应用系统。系统围绕毕业生求职就业这一核心场景，构建了个人中心、毕业生管理、企业管理、高校管理等十三个功能模块，通过智能推荐算法实现毕业生与合适岗位的精准匹配。系统支持毕业生用户注册个人信息、上传简历资料，企业用户发布岗位需求、查看求职者信息，管理员统一管理系统用户和数据。在技术实现上，后端用Django框架提供RESTful API接口，前端通过Vue.js实现数据双向绑定和组件化开发，ElementUI提供美观的UI组 件库，整体界面简洁友好。系统的智能推荐功能基于用户画像和岗位特征进行匹配计算，能够根据毕业生的专业背景、技能水平、地域偏好等因素，向其推荐合适的就业岗位 ，同时为企业推荐匹配度高的求职者，提高双方匹配效率。
三、毕业生就业智能推荐信息系统-视频解说
计算机专业毕设展览：毕业生就业智能推荐信息系统，支持Java＋Pytgon双开发，功能完善，模块充足！
四、毕业生就业智能推荐信息系统-功能展示
五、毕业生就业智能推荐信息系统-代码展示
from pyspark.sql import SparkSession
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
from django.utils.decorators import method_decorator
from django.views import View
import json
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
from .models import Graduate, Job, Resume, Interview, Company
@method_decorator(csrf_exempt, name='dispatch')
class IntelligentRecommendView(View):
def post(self, request):
spark = SparkSession.builder.appName("JobRecommendSystem").config("s
park.sql.adaptive.enabled", "true").getOrCreate()
data = json.loads(request.body)
graduate_id = data.get('graduate_id')
graduate = Graduate.objects.get(id=graduate_id)
jobs = Job.objects.filter(status=1).values('id', 'title',
'requirements', 'salary', 'location', 'company_id')
job_df = pd.DataFrame(list(jobs))
spark_df = spark.createDataFrame(job_df)
graduate_skills = graduate.skills if graduate.skills else ""
graduate_major = graduate.major if graduate.major else ""
graduate_location = graduate.preferred_location if
graduate.preferred_location else ""
user_profile = f"{graduate_skills} {graduate_major}
{graduate_location}"
job_texts = []
for job in jobs:
job_text = f"{job['title']} {job['requirements']}
{job['location']}"
job_texts.append(job_text)
all_texts = [user_profile] + job_texts
vectorizer = TfidfVectorizer(stop_words='english',
max_features=1000)
tfidf_matrix = vectorizer.fit_transform(all_texts)
user_vector = tfidf_matrix[0]
job_vectors = tfidf_matrix[1:]
similarities = cosine_similarity(user_vector, job_vectors).flatten()
job_scores = list(zip(range(len(jobs)), similarities))
job_scores.sort(key=lambda x: x[1], reverse=True)
recommended_jobs = []
for i, score in job_scores[:10]:
job_info = list(jobs)[i]
job_info['similarity_score'] = float(score)
recommended_jobs.append(job_info)
spark.stop()
return JsonResponse({'status': 'success', 'recommended_jobs':
recommended_jobs})
@method_decorator(csrf_exempt, name='dispatch')
class ResumeAnalysisView(View):
def post(self, request):
spark = SparkSession.builder.appName("ResumeAnalysis").config("spark
.sql.adaptive.enabled", "true").getOrCreate()
data = json.loads(request.body)
resume_id = data.get('resume_id')
resume = Resume.objects.get(id=resume_id)
resume_content = resume.content if resume.content else ""
education = resume.education if resume.education else ""
experience = resume.work_experience if resume.work_experience else
""
skills = resume.skills if resume.skills else ""
full_resume_text = f"{resume_content} {education} {experience}
{skills}"
resume_data = [{'id': resume.id, 'content': full_resume_text,
'graduate_id': resume.graduate_id}]
resume_df = pd.DataFrame(resume_data)
spark_df = spark.createDataFrame(resume_df)
skill_keywords = ['python', 'java', 'javascript', 'sql', 'html',
'css', 'react', 'vue', 'django', 'spring']
education_keywords = ['本科', '硕士', '博士', '985', '211', '一本',
'二本']
experience_keywords = ['实习', '项目', '开发', '设计', '管理',
'团队', '领导']
skill_score = sum(1 for keyword in skill_keywords if keyword.lower()
in full_resume_text.lower())
education_score = sum(1 for keyword in education_keywords if keyword
in full_resume_text)
experience_score = sum(1 for keyword in experience_keywords if
keyword in full_resume_text)
total_score = skill_score * 0.4 + education_score * 0.3 +
experience_score * 0.3
resume_quality = "优秀" if total_score >= 8 else "良好" if
total_score >= 5 else "一般"
improvement_suggestions = []
if skill_score < 3:
improvement_suggestions.append("建议补充更多技能关键词")
if experience_score < 2:
improvement_suggestions.append("建议增加项目经验描述")
if education_score < 1:
improvement_suggestions.append("建议完善教育背景信息")
resume.analysis_score = total_score
resume.quality_level = resume_quality
resume.save()
spark.stop()
return JsonResponse({'status': 'success', 'quality_level':
resume_quality, 'score': total_score, 'suggestions':
improvement_suggestions})
@method_decorator(csrf_exempt, name='dispatch')
class InterviewResultAnalysisView(View):
def post(self, request):
spark = SparkSession.builder.appName("InterviewAnalysis").config("sp
ark.sql.adaptive.enabled", "true").getOrCreate()
data = json.loads(request.body)
company_id = data.get('company_id')
interviews =
Interview.objects.filter(company_id=company_id).values('id', 'graduate_id',
'result', 'score', 'feedback', 'interview_date')
interview_df = pd.DataFrame(list(interviews))
if interview_df.empty:
spark.stop()
return JsonResponse({'status': 'error', 'message':
'暂无面试数据'})
spark_df = spark.createDataFrame(interview_df)
total_interviews = len(interviews)
passed_interviews = len([i for i in interviews if i['result'] ==
'通过'])
pass_rate = (passed_interviews / total_interviews * 100) if
total_interviews > 0 else 0
average_score = sum([i['score'] for i in interviews if i['score']])
/ len([i for i in interviews if i['score']]) if any(i['score'] for i in
interviews) else 0
score_distribution = {'优秀': 0, '良好': 0, '一般': 0, '较差': 0}
for interview in interviews:
if interview['score']:
if interview['score'] >= 90:
score_distribution['优秀'] += 1
elif interview['score'] >= 80:
score_distribution['良好'] += 1
elif interview['score'] >= 70:
score_distribution['一般'] += 1
else:
score_distribution['较差'] += 1
common_feedback = []
feedback_texts = [i['feedback'] for i in interviews if
i['feedback']]
if feedback_texts:
feedback_keywords = ['技术能力', '沟通能力', '团队合作',
'学习能力', '项目经验']
for keyword in feedback_keywords:
count = sum(1 for feedback in feedback_texts if keyword in
feedback)
if count > len(feedback_texts) * 0.3:
common_feedback.append(keyword)
analysis_result = {
'total_interviews': total_interviews,
'pass_rate': round(pass_rate, 2),
'average_score': round(average_score, 2),
'score_distribution': score_distribution,
'common_feedback': common_feedback
}
spark.stop()
return JsonResponse({'status': 'success', 'analysis':
analysis_result})
六、毕业生就业智能推荐信息系统-文档展示
七、END
💕💕文末获取源码联系计算机编程果茶熊
