这是使用Apache Lucene的可能解决方案。我没有使用最新版本，而是使用3.6.2版本，因为这是我最了解的版本。除了 ，别忘了将下载的存档中的 添加到项目中：它包含特定于语言的分析器（尤其是您情况下的英语分析器）。/lucene-core-x.x.x.jar/contrib/analyzers/common/lucene-analyzers-x.x.x.jar

请注意，这将仅根据输入文本单词各自的词干查找其频率。将这些频率与英语语言统计数据进行比较应在之后完成（顺便说一句，这个答案可能会有所帮助）。

数据模型

一个词干对应一个关键字。不同的单词可能具有相同的词干，因此是集合。每次找到新术语时，关键字频率都会递增（即使已经找到 - 一组会自动删除重复项）。terms

public class Keyword implements Comparable<Keyword> {

  private final String stem;
  private final Set<String> terms = new HashSet<String>();
  private int frequency = 0;

  public Keyword(String stem) {
    this.stem = stem;
  }

  public void add(String term) {
    terms.add(term);
    frequency++;
  }

  @Override
  public int compareTo(Keyword o) {
    // descending order
    return Integer.valueOf(o.frequency).compareTo(frequency);
  }

  @Override
  public boolean equals(Object obj) {
    if (this == obj) {
      return true;
    } else if (!(obj instanceof Keyword)) {
      return false;
    } else {
      return stem.equals(((Keyword) obj).stem);
    }
  }

  @Override
  public int hashCode() {
    return Arrays.hashCode(new Object[] { stem });
  }

  public String getStem() {
    return stem;
  }

  public Set<String> getTerms() {
    return terms;
  }

  public int getFrequency() {
    return frequency;
  }

}

公用事业

要对单词进行词干分析：

public static String stem(String term) throws IOException {

  TokenStream tokenStream = null;
  try {

    // tokenize
    tokenStream = new ClassicTokenizer(Version.LUCENE_36, new StringReader(term));
    // stem
    tokenStream = new PorterStemFilter(tokenStream);

    // add each token in a set, so that duplicates are removed
    Set<String> stems = new HashSet<String>();
    CharTermAttribute token = tokenStream.getAttribute(CharTermAttribute.class);
    tokenStream.reset();
    while (tokenStream.incrementToken()) {
      stems.add(token.toString());
    }

    // if no stem or 2+ stems have been found, return null
    if (stems.size() != 1) {
      return null;
    }
    String stem = stems.iterator().next();
    // if the stem has non-alphanumerical chars, return null
    if (!stem.matches("[a-zA-Z0-9-]+")) {
      return null;
    }

    return stem;

  } finally {
    if (tokenStream != null) {
      tokenStream.close();
    }
  }

}

要搜索到集合中（将由潜在关键字列表使用）：

public static <T> T find(Collection<T> collection, T example) {
  for (T element : collection) {
    if (element.equals(example)) {
      return element;
    }
  }
  collection.add(example);
  return example;
}

核心

以下是主要的输入法：

public static List<Keyword> guessFromString(String input) throws IOException {

  TokenStream tokenStream = null;
  try {

    // hack to keep dashed words (e.g. "non-specific" rather than "non" and "specific")
    input = input.replaceAll("-+", "-0");
    // replace any punctuation char but apostrophes and dashes by a space
    input = input.replaceAll("[\\p{Punct}&&[^'-]]+", " ");
    // replace most common english contractions
    input = input.replaceAll("(?:'(?:[tdsm]|[vr]e|ll))+\\b", "");

    // tokenize input
    tokenStream = new ClassicTokenizer(Version.LUCENE_36, new StringReader(input));
    // to lowercase
    tokenStream = new LowerCaseFilter(Version.LUCENE_36, tokenStream);
    // remove dots from acronyms (and "'s" but already done manually above)
    tokenStream = new ClassicFilter(tokenStream);
    // convert any char to ASCII
    tokenStream = new ASCIIFoldingFilter(tokenStream);
    // remove english stop words
    tokenStream = new StopFilter(Version.LUCENE_36, tokenStream, EnglishAnalyzer.getDefaultStopSet());

    List<Keyword> keywords = new LinkedList<Keyword>();
    CharTermAttribute token = tokenStream.getAttribute(CharTermAttribute.class);
    tokenStream.reset();
    while (tokenStream.incrementToken()) {
      String term = token.toString();
      // stem each term
      String stem = stem(term);
      if (stem != null) {
        // create the keyword or get the existing one if any
        Keyword keyword = find(keywords, new Keyword(stem.replaceAll("-0", "-")));
        // add its corresponding initial token
        keyword.add(term.replaceAll("-0", "-"));
      }
    }

    // reverse sort by frequency
    Collections.sort(keywords);

    return keywords;

  } finally {
    if (tokenStream != null) {
      tokenStream.close();
    }
  }

}

例

使用Java维基百科文章介绍部分的方法，以下是找到的前10个最常见的关键字（即词干）：guessFromString

java         x12    [java]
compil       x5     [compiled, compiler, compilers]
sun          x5     [sun]
develop      x4     [developed, developers]
languag      x3     [languages, language]
implement    x3     [implementation, implementations]
applic       x3     [application, applications]
run          x3     [run]
origin       x3     [originally, original]
gnu          x3     [gnu]

通过获取集合（在上面的示例中显示在括号之间）来循环访问输出列表，以了解每个词干的原始找到的单词。terms[...]

下一步是什么

将词干频率/频率总和比率与英语统计进行比较，如果您管理它，请让我保持在循环中：我也可能非常感兴趣:)

上面建议的代码的更新和即用型版本。
此代码与 5.x 兼容。6.x.Apache Lucene

卡字类：

import java.util.HashSet;
import java.util.Set;

/**
 * Keyword card with stem form, terms dictionary and frequency rank
 */
class CardKeyword implements Comparable<CardKeyword> {

    /**
     * Stem form of the keyword
     */
    private final String stem;

    /**
     * Terms dictionary
     */
    private final Set<String> terms = new HashSet<>();

    /**
     * Frequency rank
     */
    private int frequency;

    /**
     * Build keyword card with stem form
     *
     * @param stem
     */
    public CardKeyword(String stem) {
        this.stem = stem;
    }

    /**
     * Add term to the dictionary and update its frequency rank
     *
     * @param term
     */
    public void add(String term) {
        this.terms.add(term);
        this.frequency++;
    }

    /**
     * Compare two keywords by frequency rank
     *
     * @param keyword
     * @return int, which contains comparison results
     */
    @Override
    public int compareTo(CardKeyword keyword) {
        return Integer.valueOf(keyword.frequency).compareTo(this.frequency);
    }

    /**
     * Get stem's hashcode
     *
     * @return int, which contains stem's hashcode
     */
    @Override
    public int hashCode() {
        return this.getStem().hashCode();
    }

    /**
     * Check if two stems are equal
     *
     * @param o
     * @return boolean, true if two stems are equal
     */
    @Override
    public boolean equals(Object o) {

        if (this == o) return true;

        if (!(o instanceof CardKeyword)) return false;

        CardKeyword that = (CardKeyword) o;

        return this.getStem().equals(that.getStem());
    }

    /**
     * Get stem form of keyword
     *
     * @return String, which contains getStemForm form
     */
    public String getStem() {
        return this.stem;
    }

    /**
     * Get terms dictionary of the stem
     *
     * @return Set<String>, which contains set of terms of the getStemForm
     */
    public Set<String> getTerms() {
        return this.terms;
    }

    /**
     * Get stem frequency rank
     *
     * @return int, which contains getStemForm frequency
     */
    public int getFrequency() {
        return this.frequency;
    }
}

关键字提取器类：

import org.apache.lucene.analysis.TokenStream;
import org.apache.lucene.analysis.core.LowerCaseFilter;
import org.apache.lucene.analysis.core.StopFilter;
import org.apache.lucene.analysis.en.EnglishAnalyzer;
import org.apache.lucene.analysis.en.PorterStemFilter;
import org.apache.lucene.analysis.miscellaneous.ASCIIFoldingFilter;
import org.apache.lucene.analysis.standard.ClassicFilter;
import org.apache.lucene.analysis.standard.StandardTokenizer;
import org.apache.lucene.analysis.tokenattributes.CharTermAttribute;

import java.io.IOException;
import java.io.StringReader;
import java.util.*;

/**
 * Keywords extractor functionality handler
 */
class KeywordsExtractor {

    /**
     * Get list of keywords with stem form, frequency rank, and terms dictionary
     *
     * @param fullText
     * @return List<CardKeyword>, which contains keywords cards
     * @throws IOException
     */
    static List<CardKeyword> getKeywordsList(String fullText) throws IOException {

        TokenStream tokenStream = null;

        try {
            // treat the dashed words, don't let separate them during the processing
            fullText = fullText.replaceAll("-+", "-0");

            // replace any punctuation char but apostrophes and dashes with a space
            fullText = fullText.replaceAll("[\\p{Punct}&&[^'-]]+", " ");

            // replace most common English contractions
            fullText = fullText.replaceAll("(?:'(?:[tdsm]|[vr]e|ll))+\\b", "");

            StandardTokenizer stdToken = new StandardTokenizer();
            stdToken.setReader(new StringReader(fullText));

            tokenStream = new StopFilter(new ASCIIFoldingFilter(new ClassicFilter(new LowerCaseFilter(stdToken))), EnglishAnalyzer.getDefaultStopSet());
            tokenStream.reset();

            List<CardKeyword> cardKeywords = new LinkedList<>();

            CharTermAttribute token = tokenStream.getAttribute(CharTermAttribute.class);

            while (tokenStream.incrementToken()) {

                String term = token.toString();
                String stem = getStemForm(term);

                if (stem != null) {
                    CardKeyword cardKeyword = find(cardKeywords, new CardKeyword(stem.replaceAll("-0", "-")));
                    // treat the dashed words back, let look them pretty
                    cardKeyword.add(term.replaceAll("-0", "-"));
                }
            }

            // reverse sort by frequency
            Collections.sort(cardKeywords);

            return cardKeywords;
        } finally {
            if (tokenStream != null) {
                try {
                    tokenStream.close();
                } catch (IOException e) {
                    e.printStackTrace();
                }
            }
        }
    }

    /**
     * Get stem form of the term
     *
     * @param term
     * @return String, which contains the stemmed form of the term
     * @throws IOException
     */
    private static String getStemForm(String term) throws IOException {

        TokenStream tokenStream = null;

        try {
            StandardTokenizer stdToken = new StandardTokenizer();
            stdToken.setReader(new StringReader(term));

            tokenStream = new PorterStemFilter(stdToken);
            tokenStream.reset();

            // eliminate duplicate tokens by adding them to a set
            Set<String> stems = new HashSet<>();

            CharTermAttribute token = tokenStream.getAttribute(CharTermAttribute.class);

            while (tokenStream.incrementToken()) {
                stems.add(token.toString());
            }

            // if stem form was not found or more than 2 stems have been found, return null
            if (stems.size() != 1) {
                return null;
            }

            String stem = stems.iterator().next();

            // if the stem form has non-alphanumerical chars, return null
            if (!stem.matches("[a-zA-Z0-9-]+")) {
                return null;
            }

            return stem;
        } finally {
            if (tokenStream != null) {
                try {
                    tokenStream.close();
                } catch (IOException e) {
                    e.printStackTrace();
                }
            }
        }
    }

    /**
     * Find sample in collection
     *
     * @param collection
     * @param sample
     * @param <T>
     * @return <T> T, which contains the found object within collection if exists, otherwise the initially searched object
     */
    private static <T> T find(Collection<T> collection, T sample) {

        for (T element : collection) {
            if (element.equals(sample)) {
                return element;
            }
        }

        collection.add(sample);

        return sample;
    }
}

函数调用：

String text = "…";
List<CardKeyword> keywordsList = KeywordsExtractor.getKeywordsList(text);