详细代码智能简历解析系统Java

Java代码实现智能简历解析系统，需要用到自然语言处理（NLP）和机器学习技术，主要步骤如下：

1. 数据采集和预处理：从各种渠道收集大量的简历数据，并进行数据清洗和预处理，去除无用信息，提取关键信息，例如姓名、联系方式、教育经历、工作经历等。

2. 特征提取：根据预处理后的数据，提取出重要的特征，例如学历、专业、工作年限、技能等，并将其转化为数值型特征，以便于机器学习算法处理。

3. 模型训练和选择：选择合适的机器学习算法，例如决策树、随机森林、逻辑回归等，根据提取的特征对模型进行训练，以实现从简历中提取关键信息的目的。

4. 简历解析：使用训练好的模型对新的简历进行解析，提取出相应的信息，例如姓名、联系方式、教育经历、工作经历等，并将其存储到数据库中。

5. 智能推荐：根据解析后的信息，结合职位需求，进行匹配和推荐合适的候选人，以提高招聘效率和准确性。

下面是一个简单的Java实现示例：

// 数据预处理
public class ResumePreprocessor {
    public static Resume preprocess(String resumeText) {
        // 去除非文字信息，例如图片、表格等
        String text = removeNonTextInformation(resumeText);
        // 提取关键信息，例如姓名、联系方式、教育经历、工作经历等
        String name = extractName(text);
        String email = extractEmail(text);
        List<Education> educationList = extractEducation(text);
        List<Experience> experienceList = extractExperience(text);
        // 构建简历对象
        Resume resume = new Resume(name, email, educationList, experienceList);
        return resume;
    }
}

// 特征提取
public class ResumeFeatureExtractor {
    public static Map<String, Double> extractFeatures(Resume resume) {
        Map<String, Double> featureMap = new HashMap<>();
        // 提取学历、专业、工作年限、技能等特征，并转化为数值型特征
        double educationLevel = extractEducationLevel(resume);
        double workYears = extractWorkYears(resume);
        double skillLevel = extractSkillLevel(resume);
        featureMap.put("educationLevel", educationLevel);
        featureMap.put("workYears", workYears);
        featureMap.put("skillLevel", skillLevel);
        return featureMap;
    }
}

// 模型训练和选择
public class ResumeClassifier {
    private static final String MODEL_FILE = "resume_classifier.model";
    private static final List<String> FEATURE_LIST = Arrays.asList("educationLevel", "workYears", "skillLevel");
    private DecisionTreeModel model;

    public ResumeClassifier() {
        // 加载训练好的模型
        model = DecisionTreeModel.load(MODEL_FILE);
    }

    public boolean classify(Resume resume) {
        // 提取特征
        Map<String, Double> featureMap = ResumeFeatureExtractor.extractFeatures(resume);
        List<LabeledPoint> data = new ArrayList<>();
        // 将特征转化为LabeledPoint类型，以便于模型预测
        LabeledPoint labeledPoint = new LabeledPoint(1.0, Vectors.dense(
                featureMap.get("educationLevel"),
                featureMap.get("workYears"),
                featureMap.get("skillLevel")
        ));
        data.add(labeledPoint);
        Dataset<Row> testData = SparkUtils.spark().createDataFrame(data, LabeledPoint.class).toDF();
        // 使用模型进行预测
        double prediction = model.predict(testData.head().features());
        return prediction == 1.0;
    }
}

// 简历解析
public class ResumeParser {
    private static final String RESUME_FOLDER = "resumes";
    private static final String DATABASE_URL = "jdbc:mysql://localhost:3306/resume_db";
    private static final String USERNAME = "root";
    private static final String PASSWORD = "password";
    private static final String DRIVER_CLASS = "com.mysql.jdbc.Driver";
    private static final String INSERT_SQL = "INSERT INTO resume (name, email, education, experience) VALUES (?, ?, ?, ?)";

    public static void main(String[] args) {
        // 获取简历文件列表
        List<File> resumeFiles = getResumeFiles(RESUME_FOLDER);
        // 初始化数据库连接
        try (Connection conn = DriverManager.getConnection(DATABASE_URL, USERNAME, PASSWORD)) {
            // 解析每个简历文件，并将其存储到数据库中
            for (File resumeFile : resumeFiles) {
                String resumeText = FileUtils.readFileToString(resumeFile, StandardCharsets.UTF_8);
                Resume resume = ResumePreprocessor.preprocess(resumeText);
                if (resume != null) {
                    boolean isQualified = new ResumeClassifier().classify(resume);
                    if (isQualified) {
                        PreparedStatement stmt = conn.prepareStatement(INSERT_SQL);
                        stmt.setString(1, resume.getName());
                        stmt.setString(2, resume.getEmail());
                        stmt.setString(3, gson.toJson(resume.getEducationList()));
                        stmt.setString(4, gson.toJson(resume.getExperienceList()));
                        stmt.executeUpdate();
                    }
                }
            }
        } catch (SQLException | IOException e) {
            e.printStackTrace();
        }
    }
}

// 智能推荐
public class CandidateMatcher {
    private static final String DATABASE_URL = "jdbc:mysql://localhost:3306/resume_db";
    private static final String USERNAME = "root";
    private static final String PASSWORD = "password";
    private static final String DRIVER_CLASS = "com.mysql.jdbc.Driver";
    private static final String SELECT_SQL = "SELECT * FROM resume WHERE education LIKE ? AND experience LIKE ?";

    public static List<Resume> findMatches(String education, String experience) {
        List<Resume> matches = new ArrayList<>();
        // 初始化数据库连接
        try (Connection conn = DriverManager.getConnection(DATABASE_URL, USERNAME, PASSWORD)) {
            PreparedStatement stmt = conn.prepareStatement(SELECT_SQL);
            stmt.setString(1, "%" + education + "%");
            stmt.setString(2, "%" + experience + "%");
            ResultSet rs = stmt.executeQuery();
            // 将匹配的简历转化为对象
            while (rs.next()) {
                String name = rs.getString("name");
                String email = rs.getString("email");
                String educationJson = rs.getString("education");
                String experienceJson = rs.getString("experience");
                List<Education> educationList = gson.fromJson(educationJson, new TypeToken<List<Education>>() {}.getType());
                List<Experience> experienceList = gson.fromJson(experienceJson, new TypeToken<List<Experience>>() {}.getType());
                Resume resume = new Resume(name, email, educationList, experienceList);
                matches.add(resume);
            }
        } catch (SQLException e) {
            e.printStackTrace();
        }
        return matches;
    }
}

以上代码仅供参考，实际实现需要根据具体需求进行调整和优化。