GitHub高星项目启发：基于随机森林的Boss直聘数据分析系统，毕设通过率最高💖💖作者：计算机编程小咖 💙💙个

💖💖作者：计算机编程小咖 💙💙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习实战项目

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基于随机森林算法的Boss直聘数据分析及可视化系统介绍

基于随机森林算法的Boss直聘数据分析及可视化系统是一个集数据采集、智能分析、薪资预测和可视化展示于一体的综合性毕业设计项目。该系统采用Java+SpringBoot或Python+Django双技术栈架构，前端使用Vue+ElementUI构建现代化用户界面，MySQL作为数据存储引擎，全面支持B/S架构部署。系统核心功能围绕Boss直聘招聘数据展开，通过随机森林机器学习算法对海量招聘信息进行深度挖掘和分析，实现精准的薪资预测功能。用户可通过系统首页快速了解平台概况，个人中心支持密码修改和个人信息管理，管理员端提供完整的用户管理和招聘信息维护功能。系统最大亮点在于数据看板模块，通过丰富的图表和可视化组件，直观展示招聘市场趋势、薪资分布、岗位热度等关键指标，为求职者和HR提供数据驱动的决策支持。此外，系统还配备了轮播图管理、公告信息发布、系统简介等辅助功能模块，确保平台运营的完整性和用户体验的流畅性。整个系统将传统的招聘数据静态展示升级为基于机器学习算法的智能分析平台，充分体现了大数据时代下数据分析技术在人力资源领域的创新应用价值。

基于随机森林算法的Boss直聘数据分析及可视化系统演示视频

演示视频

基于随机森林算法的Boss直聘数据分析及可视化系统演示图片

登陆界面.png

数据看板.png

系统首页.png

薪资预测.png

用户管理.png

招聘信息.png

基于随机森林算法的Boss直聘数据分析及可视化系统代码展示

import org.apache.spark.sql.SparkSession;
import org.apache.spark.ml.feature.VectorAssembler;
import org.apache.spark.ml.regression.RandomForestRegressor;
import org.apache.spark.ml.regression.RandomForestRegressionModel;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.springframework.web.bind.annotation.*;
import java.util.*;

@RestController
@RequestMapping("/api")
public class BossDataAnalysisController {
   
   private SparkSession spark = SparkSession.builder().appName("BossDataAnalysis").master("local[*]").getOrCreate();
   
   @PostMapping("/salary/predict")
   public Map<String, Object> predictSalary(@RequestBody Map<String, Object> jobData) {
       Dataset<Row> trainingData = spark.sql("SELECT * FROM boss_jobs WHERE salary > 0");
       String[] featureColumns = {"experience_years", "education_level", "company_size", "industry_code", "city_level"};
       VectorAssembler assembler = new VectorAssembler().setInputCols(featureColumns).setOutputCol("features");
       Dataset<Row> featureData = assembler.transform(trainingData);
       RandomForestRegressor rf = new RandomForestRegressor().setLabelCol("salary").setFeaturesCol("features").setNumTrees(100);
       RandomForestRegressionModel model = rf.fit(featureData);
       Dataset<Row> inputData = spark.createDataFrame(Arrays.asList(jobData), Object.class);
       Dataset<Row> inputFeatures = assembler.transform(inputData);
       Dataset<Row> predictions = model.transform(inputFeatures);
       double predictedSalary = predictions.select("prediction").first().getDouble(0);
       double confidenceScore = model.featureImportances().toArray()[0] * 100;
       Map<String, Object> result = new HashMap<>();
       result.put("predicted_salary", Math.round(predictedSalary));
       result.put("confidence", Math.round(confidenceScore * 100.0) / 100.0);
       result.put("salary_range_min", Math.round(predictedSalary * 0.8));
       result.put("salary_range_max", Math.round(predictedSalary * 1.2));
       result.put("market_level", predictedSalary > 15000 ? "高薪" : predictedSalary > 8000 ? "中等" : "入门");
       return result;
   }
   
   @GetMapping("/dashboard/analysis")
   public Map<String, Object> getDashboardData() {
       Dataset<Row> allJobs = spark.sql("SELECT * FROM boss_jobs WHERE created_date >= DATE_SUB(CURRENT_DATE, 30)");
       Dataset<Row> salaryStats = spark.sql("SELECT AVG(salary) as avg_salary, MIN(salary) as min_salary, MAX(salary) as max_salary, COUNT(*) as total_jobs FROM boss_jobs");
       Row salaryRow = salaryStats.first();
       Dataset<Row> industryData = spark.sql("SELECT industry_name, COUNT(*) as job_count, AVG(salary) as avg_salary FROM boss_jobs GROUP BY industry_name ORDER BY job_count DESC LIMIT 10");
       List<Row> industryList = industryData.collectAsList();
       Dataset<Row> cityData = spark.sql("SELECT city_name, COUNT(*) as job_count, AVG(salary) as avg_salary FROM boss_jobs GROUP BY city_name ORDER BY job_count DESC LIMIT 15");
       List<Row> cityList = cityData.collectAsList();
       Dataset<Row> trendData = spark.sql("SELECT DATE(created_date) as job_date, COUNT(*) as daily_count FROM boss_jobs WHERE created_date >= DATE_SUB(CURRENT_DATE, 7) GROUP BY DATE(created_date) ORDER BY job_date");
       List<Row> trendList = trendData.collectAsList();
       Map<String, Object> dashboard = new HashMap<>();
       dashboard.put("total_jobs", salaryRow.getLong("total_jobs"));
       dashboard.put("avg_salary", Math.round(salaryRow.getDouble("avg_salary")));
       dashboard.put("min_salary", Math.round(salaryRow.getDouble("min_salary")));
       dashboard.put("max_salary", Math.round(salaryRow.getDouble("max_salary")));
       dashboard.put("industry_distribution", industryList);
       dashboard.put("city_distribution", cityList);
       dashboard.put("weekly_trend", trendList);
       dashboard.put("hot_keywords", getHotKeywords());
       return dashboard;
   }
   
   @PostMapping("/jobs/intelligent-match")
   public Map<String, Object> intelligentJobMatch(@RequestBody Map<String, Object> userProfile) {
       Dataset<Row> userSkills = spark.createDataFrame(Arrays.asList(userProfile), Object.class);
       Dataset<Row> jobFeatures = spark.sql("SELECT job_id, job_title, company_name, salary, skills_required, experience_required FROM boss_jobs WHERE status = 'active'");
       String userSkillSet = (String) userProfile.get("skills");
       int userExperience = (Integer) userProfile.get("experience_years");
       String userEducation = (String) userProfile.get("education");
       Dataset<Row> matchedJobs = spark.sql(String.format("SELECT *, CASE WHEN skills_required LIKE '%%%s%%' THEN 3 ELSE 0 END + CASE WHEN experience_required <= %d THEN 2 ELSE 0 END + CASE WHEN education_required <= '%s' THEN 1 ELSE 0 END as match_score FROM boss_jobs WHERE status = 'active' HAVING match_score > 0 ORDER BY match_score DESC, salary DESC LIMIT 20", userSkillSet, userExperience, userEducation));
       List<Row> jobList = matchedJobs.collectAsList();
       Dataset<Row> similarUsers = spark.sql(String.format("SELECT recommended_jobs FROM user_profiles WHERE skills LIKE '%%%s%%' AND experience_years BETWEEN %d AND %d", userSkillSet, userExperience-1, userExperience+1));
       List<String> recommendedJobIds = new ArrayList<>();
       similarUsers.collectAsList().forEach(row -> {
           String jobs = row.getString("recommended_jobs");
           if (jobs != null) recommendedJobIds.addAll(Arrays.asList(jobs.split(",")));
       });
       Dataset<Row> collaborativeJobs = spark.sql(String.format("SELECT * FROM boss_jobs WHERE job_id IN (%s) ORDER BY salary DESC LIMIT 10", String.join(",", recommendedJobIds)));
       List<Row> collaborativeList = collaborativeJobs.collectAsList();
       Map<String, Object> matchResult = new HashMap<>();
       matchResult.put("content_based_matches", jobList);
       matchResult.put("collaborative_recommendations", collaborativeList);
       matchResult.put("match_summary", String.format("基于您的技能找到%d个匹配职位", jobList.size()));
       matchResult.put("avg_match_salary", jobList.stream().mapToDouble(row -> row.getDouble("salary")).average().orElse(0.0));
       return matchResult;
   }
   
   private List<String> getHotKeywords() {
       Dataset<Row> keywords = spark.sql("SELECT keyword, COUNT(*) as frequency FROM job_keywords WHERE created_date >= DATE_SUB(CURRENT_DATE, 7) GROUP BY keyword ORDER BY frequency DESC LIMIT 20");
       return keywords.select("keyword").as(org.apache.spark.sql.Encoders.STRING()).collectAsList();
   }
}

基于随机森林算法的Boss直聘数据分析及可视化系统文档展示

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