[Java Web]敏感词过滤算法

1.DFA算法

DFA算法的原理可以参考 这里，简单来说就是通过Map构造出一颗敏感词树，树的每一条由根节点到叶子节点的路径构成一个敏感词，例如下图：

代码简单实现如下：

 

public class TextFilterUtil {

    //日志
    private static final Logger LOG = LoggerFactory.getLogger(TextFilterUtil.class);
    //敏感词库
    private static HashMap sensitiveWordMap = null;
    //默认编码格式
    private static final String ENCODING = "gbk";
    //敏感词库的路径
    private static final InputStream in = TextFilterUtil.class.getClassLoader().getResourceAsStream("sensitive/keyWords.txt");

    /**
     * 初始化敏感词库
     */
    private static void init() {
        //读取文件
        Set<String> keyWords = readSensitiveWords();
        //创建敏感词库
        sensitiveWordMap = new HashMap<>(keyWords.size());
        for (String keyWord : keyWords) {
            createKeyWord(keyWord);
        }
    }

    /**
     * 构建敏感词库
     *
     * @param keyWord
     */
    private static void createKeyWord(String keyWord) {
        if (sensitiveWordMap == null) {
            LOG.error("sensitiveWordMap 未初始化!");
            return;
        }
        Map nowMap = sensitiveWordMap;
        for (Character c : keyWord.toCharArray()) {
            Object obj = nowMap.get(c);
            if (obj == null) {
                Map<String, Object> childMap = new HashMap<>();
                childMap.put("isEnd", "false");
                nowMap.put(c, childMap);
                nowMap = childMap;
            } else {
                nowMap = (Map) obj;
            }
        }
        nowMap.put("isEnd", "true");
    }

    /**
     * 读取敏感词文件
     *
     * @return
     */
    private static Set<String> readSensitiveWords() {
        Set<String> keyWords = new HashSet<>();
        BufferedReader reader = null;
        try {
            reader = new BufferedReader(new InputStreamReader(in, ENCODING));
            String line;
            while ((line = reader.readLine()) != null) {
                keyWords.add(line.trim());
            }
        } catch (UnsupportedEncodingException e) {
            LOG.error("敏感词库文件转码失败!");
        } catch (FileNotFoundException e) {
            LOG.error("敏感词库文件不存在!");
        } catch (IOException e) {
            LOG.error("敏感词库文件读取失败!");
        } finally {
            if (reader != null) {
                try {
                    reader.close();
                } catch (IOException e) {
                    e.printStackTrace();
                }
                reader = null;
            }
        }
        return keyWords;
    }

    /**
     * 检查敏感词
     *
     * @return
     */
    private static List<String> checkSensitiveWord(String text) {
        if (sensitiveWordMap == null) {
            init();
        }
        List<String> sensitiveWords = new ArrayList<>();
        Map nowMap = sensitiveWordMap;
        for (int i = 0; i < text.length(); i++) {
            Character word = text.charAt(i);
            Object obj = nowMap.get(word);
            if (obj == null) {
                continue;
            }
            int j = i + 1;
            Map childMap = (Map) obj;
            while (j < text.length()) {
                if ("true".equals(childMap.get("isEnd"))) {
                    sensitiveWords.add(text.substring(i, j));
                }
                obj = childMap.get(text.charAt(j));
                if (obj != null) {
                    childMap = (Map) obj;
                } else {
                    break;
                }
                j++;
            }
        }
        return sensitiveWords;
    }
}

 

 

2.TTMP算法

TTMP算法由网友原创，关于它的起源可以查看 这里，TTMP算法的原理是将敏感词拆分成“脏字”的序列，只有待比对字符串完全由“脏字”组成时，才去判断它是否为敏感词，减少了比对次数。这个算法的简单实现如下：

 

public class TextFilterUtil {

    //日志
    private static final Logger LOG = LoggerFactory.getLogger(TextFilterUtil.class);
    //默认编码格式
    private static final String ENCODING = "gbk";
    //敏感词库的路径
    private static final InputStream in = TextFilterUtil.class.getClassLoader().getResourceAsStream("sensitive/keyWords.txt");
    //脏字库
    private static Set<Character> sensitiveCharSet = null;
    //敏感词库
    private static Set<String> sensitiveWordSet = null;

    /**
     * 初始化敏感词库
     */
    private static void init() {
        //初始化容器
        sensitiveCharSet = new HashSet<>();
        sensitiveWordSet = new HashSet<>();
        //读取文件 创建敏感词库
        readSensitiveWords();
    }

    /**
     * 读取本地的敏感词文件
     *
     * @return
     */
    private static void readSensitiveWords() {
        BufferedReader reader = null;
        try {
            reader = new BufferedReader(new InputStreamReader(in, ENCODING));
            String line;
            while ((line = reader.readLine()) != null) {
                String word = line.trim();
                sensitiveWordSet.add(word);
                for (Character c : word.toCharArray()) {
                    sensitiveCharSet.add(c);
                }
            }
        } catch (UnsupportedEncodingException e) {
            LOG.error("敏感词库文件转码失败!");
        } catch (FileNotFoundException e) {
            LOG.error("敏感词库文件不存在!");
        } catch (IOException e) {
            LOG.error("敏感词库文件读取失败!");
        } finally {
            if (reader != null) {
                try {
                    reader.close();
                } catch (IOException e) {
                    e.printStackTrace();
                }
                reader = null;
            }
        }
        return;
    }

    /**
     * 检查敏感词
     *
     * @return
     */
    private static List<String> checkSensitiveWord(String text) {
        if (sensitiveWordSet == null || sensitiveCharSet == null) {
            init();
        }
        List<String> sensitiveWords = new ArrayList<>();
        for (int i = 0; i < text.length(); i++) {
            Character word = text.charAt(i);
            if (!sensitiveCharSet.contains(word)) {
                continue;
            }
            int j = i;
            while (j < text.length()) {
                if (!sensitiveCharSet.contains(word)) {
                    break;
                }
                String key = text.substring(i, j + 1);
                if (sensitiveWordSet.contains(key)) {
                    sensitiveWords.add(key);
                }
                j++;
            }
        }
        return sensitiveWords;
    }
}

注：以上代码实现仅用于展示思路，在实际使用中还有很多地方可以优化。