直观理解：Trie树（字典树/前缀树/单词查找树）

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  根据Trie树的属性，我们给出简单的Trie树创建·和查找的代码示例：

package com.quan.trie; public class Trie { // root for current trie TreeNode root; // define the TreeNode class TreeNode{ // children of current node. TreeNode[] children; // whether is end of a word. boolean isEnd; //Assume that there are only lowercase letters in the word public TreeNode (){ children = new TreeNode[26]; } } / Initialize your data structure here. */ public Trie() { // Initialize the root without value. root = new TreeNode(); } / Inserts a word into the trie. */ public void insert(String word) { if (word == null || word.length() < 1) return; TreeNode node = root; for (int i = 0, pos = 0; i < word.length(); i++, node = node.children[pos]) { pos = word.charAt(i) - 'a'; if (node.children[pos] == null){ node.children[pos] = new TreeNode(); } } node.isEnd = true; } / Returns if the word is in the trie. */ public boolean search(String word) { if (word == null || word.length() < 1) return false; TreeNode node = root; for (int i = 0, pos = 0; i < word.length(); i++, node = node.children[pos]) { pos = word.charAt(i) - 'a'; if (node.children[pos] == null){ return false; } } return node.isEnd; } / Returns if there is any word in the trie that starts with the given prefix. */ public boolean startsWith(String prefix) { if (prefix == null || prefix.length() < 1) return true; TreeNode node = root; for (int i = 0, pos = 0; i < prefix.length(); i++, node = node.children[pos]) { pos = prefix.charAt(i) - 'a'; if (node.children[pos] == null){ return false; } } return true; } }

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  Trie树可以最大限度地减少无谓的字符串比较，在Trie树中对单个长度为的单词的查找时间复杂度可以做到，且跟语料库中单词的数量没有太大关系。Trie树的使用场景主要有字符串检索、字符串字典排序、和前缀索引等。