营销型网站功能模块开户推广竞价开户

参考：HanLP 自然语言处理使用总结-CSDN博客

参考：Sprint Boot 工程中HanLP配置相对路径，始终有问题的解决方案_springboot hanlp-CSDN博客

<!--hanlp 依赖--><dependency><groupId>com.hankcs</groupId><artifactId>hanlp</artifactId><version>portable-1.8.4</version></dependency>

public class ResourceFileIoAdapter implements IIOAdapter {@Overridepublic InputStream open(String path) throws IOException {ClassPathResource resource = new ClassPathResource(path);// return Files.newInputStream(resource.getFile().toPath());// Linux环境下跑要把open()里改成这样：return resource.getInputStream();}@Overridepublic OutputStream create(String path) throws IOException {ClassPathResource resource = new ClassPathResource(path);OutputStream os = new FileOutputStream(resource.getFile());return os;}
}

 http://nlp.hankcs.com/download.php?file=data
下载后

其中数据分为词典和模型，其中词典是词法分析必需的，模型是句法分析必需的，用户可以自行增删替换，如果不需要句法分析等功能的话，随时可以删除model文件夹 

实践使用

从文本中提取关键字

// 提取名词关键字
public static String extractOptimizedKeywords(String text, int keywordCount) {// 获取短语List<String> termList = HanLP.extractPhrase(text, 100);// 只保留都是名词的短语List<String> termNounsList = filterOnlyNounsFromPhrases(termList);System.out.println(termNounsList);// 计算TF-IDF值并排序Map<String, Double> tfidfMap = calculateTfidf(termNounsList, text);List<Map.Entry<String, Double>> sortedKeywords = tfidfMap.entrySet().stream().sorted(Map.Entry.<String, Double>comparingByValue().reversed()).collect(Collectors.toList());// 返回出现次数最多的前keywordCount个复合关键字List<String> keywords = sortedKeywords.stream().limit(keywordCount).map(Map.Entry::getKey).collect(Collectors.toList());// 将关键词列表转换为逗号分隔的字符串return String.join(", ", keywords);}// 获取名词短语
public static List<String> filterOnlyNounsFromPhrases(List<String> phrases) {List<String> nounOnlyPhrases = new ArrayList<>();for (String phrase : phrases) {// 对每个短语进行分词和词性标注List<Term> terms = HanLP.segment(phrase);// 检查短语中是否所有词都是名词boolean allNouns = terms.stream().allMatch(term -> term.nature.toString().startsWith("n"));// 如果短语中所有词都是名词，则保留该短语if (allNouns) {nounOnlyPhrases.add(phrase);}}return nounOnlyPhrases;}// 计算TF-IDF值并排序
private static Map<String, Double> calculateTfidf(List<String> terms, String corpus) {Map<String, Integer> termFrequency = terms.stream().collect(Collectors.groupingBy(Function.identity(), Collectors.summingInt(v -> 1)));Map<String, Integer> documentFrequency = calculateDocumentFrequency(corpus);Map<String, Double> tfidf = new HashMap<>();for (Map.Entry<String, Integer> entry : termFrequency.entrySet()) {String term = entry.getKey();int freq = entry.getValue();int df = documentFrequency.getOrDefault(term, 1); // 确保df至少为1，避免log(0)double tf = (double) freq / terms.size();double idf = Math.log((double) 1 + corpus.length() / df); // 调整idf公式，用语料库长度作为文档数量的代理double tfidfValue = tf * idf;tfidf.put(term, tfidfValue);}return tfidf;}private static Map<String, Integer> calculateDocumentFrequency(String corpus) {List<Term> docTerms = HanLP.segment(corpus);return docTerms.stream().filter(term -> term.nature.toString().startsWith("n")) // 使用startsWith("n")匹配任何名词性质.collect(Collectors.groupingBy(term -> term.word, Collectors.summingInt(v -> 1)));}