转自：http://www.cnblogs.com/lanxuezaipiao/archive/2013/05/23/3096001.html
在www.json.org上公布了很多JAVA下的json构造和解析工具，其中org.json和json-lib比较简单，两者使用上差不多但还是有些区别。下面首先介绍用json-lib构造和解析Json数据的方法示例。
用org.son构造和解析Json数据的方法详解请参见我下一篇博文：Java构造和解析Json数据的两种方法详解二
一、介绍
JSON-lib包是一个beans,collections,maps,java arrays 和XML和JSON互相转换的包，主要就是用来解析Json数据，在其官网http://www.json.org/上有详细讲解，有兴趣的可以去研究。
二、下载jar依赖包：可以去这里下载
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三、基本方法介绍
1. List集合转换成json方法
List list = new ArrayList();
list.add( "first" );
list.add( "second" );
JSONArray jsonArray2 = JSONArray.fromObject( list );
2. Map集合转换成json方法
Map map = new HashMap();
map.put("name", "json");
map.put("bool", Boolean.TRUE);
map.put("int", new Integer(1));
map.put("arr", new String[] { "a", "b" });
map.put("func", "function(i){ return this.arr[i]; }");
JSONObject json = JSONObject.fromObject(map);
3. Bean转换成json代码
JSONObject jsonObject = JSONObject.fromObject(new JsonBean());
4. 数组转换成json代码
boolean[] boolArray = new boolean[] { true, false, true };
JSONArray jsonArray1 = JSONArray.fromObject(boolArray);
5. 一般数据转换成json代码
JSONArray jsonArray3 = JSONArray.fromObject("['json','is','easy']" );
6. beans转换成json代码
List list = new ArrayList();
JsonBean2 jb1 = new JsonBean2();
jb1.setCol(1);
jb1.setRow(1);
jb1.setValue("xx"); JsonBean2 jb2 = new JsonBean2();
jb2.setCol(2);
jb2.setRow(2);
jb2.setValue(""); list.add(jb1);
list.add(jb2);
JSONArray ja = JSONArray.fromObject(list);
四、演示示例
这里以基本的几个常用方法进行测试
package com.json; import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map; import net.sf.json.JSONArray;
import net.sf.json.JSONObject; /**
* 使用json-lib构造和解析Json数据
*
* @author Alexia
* @date 2013/5/23
*
*/
public class JsonTest { /**
* 构造Json数据
*
* @return
*/
public static String BuildJson() { // JSON格式数据解析对象
JSONObject jo = new JSONObject(); // 下面构造两个map、一个list和一个Employee对象
Map<String, String> map1 = new HashMap<String, String>();
map1.put("name", "Alexia");
map1.put("sex", "female");
map1.put("age", "23"); Map<String, String> map2 = new HashMap<String, String>();
map2.put("name", "Edward");
map2.put("sex", "male");
map2.put("age", "24"); List<Map> list = new ArrayList<Map>();
list.add(map1);
list.add(map2); Employee employee = new Employee();
employee.setName("wjl");
employee.setSex("female");
employee.setAge(24); // 将Map转换为JSONArray数据
JSONArray ja1 = JSONArray.fromObject(map1);
// 将List转换为JSONArray数据
JSONArray ja2 = JSONArray.fromObject(list);
// 将Bean转换为JSONArray数据
JSONArray ja3 = JSONArray.fromObject(employee); System.out.println("JSONArray对象数据格式：");
System.out.println(ja1.toString());
System.out.println(ja2.toString());
System.out.println(ja3.toString()); // 构造Json数据，包括一个map和一个Employee对象
jo.put("map", ja1);
jo.put("employee", ja2);
System.out.println("\n最终构造的JSON数据格式：");
System.out.println(jo.toString()); return jo.toString(); } /**
* 解析Json数据
*
* @param jsonString Json数据字符串
*/
public static void ParseJson(String jsonString) { // 以employee为例解析，map类似
JSONObject jb = JSONObject.fromObject(jsonString);
JSONArray ja = jb.getJSONArray("employee"); List<Employee> empList = new ArrayList<Employee>(); // 循环添加Employee对象（可能有多个）
for (int i = 0; i < ja.size(); i++) {
Employee employee = new Employee(); employee.setName(ja.getJSONObject(i).getString("name"));
employee.setSex(ja.getJSONObject(i).getString("sex"));
employee.setAge(ja.getJSONObject(i).getInt("age")); empList.add(employee);
} System.out.println("\n将Json数据转换为Employee对象：");
for (int i = 0; i < empList.size(); i++) {
Employee emp = empList.get(i);
System.out.println("name: " + emp.getName() + " sex: "
+ emp.getSex() + " age: " + emp.getAge());
} } /**
* @param args
*/
public static void main(String[] args) {
// TODO Auto-generated method stub ParseJson(BuildJson());
} }
运行结果如下
aaarticlea/png;base64,iVBORw0KGgoAAAANSUhEUgAABNEAAADSCAIAAADrFcGUAAAgAElEQVR4nO3d25miMAAGUOuiIOqhGpqhGPYBgQSSAA7Rcfach/0cwBDCZflNlMcIAAAAdTw+XQEAAAD+LJkz8ngkGiQ58eTb7y3w/BuXhS+9BQAA4F4CSSQZGpOS7yq8yC1/suRCaYfbInYCAACf8j+mkR9mvMLEfdfiYeYsF77/M1ntJFETAAD4uP89liwpcR9Ecz2c++B3clbyz2R99mWO+Sh7pp77FQEAALzBYRTp2yi5tP04juPYNfOEpmnnifH0LpzSdMM4jsMyu+mikueF3+9xpZ/z8M9Nuntk+jNzwXIfDjcl5JLqmU0DAAB4v7OxZOiaJVn27TNDjs8YGQTReaGha+LYGbw7TJhD17R93y7lvUMuCu6nnO88TGbCM5kzt+qTNSyQOQEAgI/7aeaMloi7K/u5A3SKlXPqjDLnc3If9pU+O0Cbbph7Qp9ry02Pe0yHuJjnlOWP52afC4ehM7M2HZj7FRVWl4yXhWSbXDJcvlxhiRQAAHiDVzLnmBpDO3TNNojO3ZfTW+f5YeZcsmYcOsc5I84xte+L04P5XRMUFJcab0E6B+Y6G/dzT/ZA5jLnMr3QfVoIrmc6RWVOAADg4wrBI+qQ3GTOcPoUOw8z59wRGhQbZMJt+bnhtsnp8XdO42y89oVu3njYhZjsNixkyH1o3C9f6IosZ+BkxC2HTKkSAAD4uJ9mznEbKcP3B2Nr5ylN1y3Fbn6eKAqL5zPnUOjaXP9OdnKe76vcdEsmKlYcMZtLg5e6LnOxtlCC2AkAAHxWKZOsYW7qzXxmvfjrnEHUzP+GUPhV0Hk47i6jRh2llzLnUk74i0ZrsYkfKcr1HyY7MMNZhbB6ssz92gthMhdKr2bOwnYBAADUUw4ea09k9BM/TdOsESlKc+sb4melPMLHpTRdtNywWVGu+zPbLRr+hNBUs21X5yP7Tc7DfDhm8uThkoUyN8H1ZH3OZM59cH2kQrLMCQAAvMd/EDziTs5y1tqktcOOx+SfZyLf+FLmzKXKZFVz9QQAAHibvx9F8l9EjZRj21hMbrlomgyf5dD4k37OApkTAAD4iL8bRaKhuOkvhwIAAFDV382cAAAAfJrMCQAAQC0yJwAAALXInAAAANQicwIAAFCLzAkAAEAtx5lz6Jr040amGeHDL6fHk7T9NOf+55NcLb+4/Lxdj81G3FbP8y1w63bd4L76rA+sabrEW354/NRuh7vW+9ry73/Cz8F+3J0l2UsDAACsTmXO5D1lYvrQNdMNaPoW9ceull9avm8rVHCz8rP34ndu1x1uqs/QNUvU7Nt1xtA1yx99GySW39YOd603vXzwANlNIJ/fdEuWi1r4lXqu5SS39K56AgDwV92fOdt+up2ucCN6tfzC8kPXpG70b3Q1c96zXbe4qT59G3eVJRs8jDK/rR3uWm/5OGz7uKHWOe/OcsXtGrpG5gQA4AW3Zs6lT2XomrhTZB3H2jRr2AuHt+4mNt2wvA66htLlz0vu+2YSywcdTPHwzn19nqMMm6nwbl+ddfFtE+UyeaprK79daZnls/VZZjRd165b8EJ9rrTzZpHkURT3nt3RDtO2PndZXNV9+5T3b/32eW5+qg/x7PFTOF9yY3RLx0m2/WVOAABec2/mPC5h6kGZXocFRHe506DMIR6FebiOxL3+QZ0209L1mSuzjBRdA0Lf9+t7t+vet092e++Sqc/6Ov4K7iv1udrO+dKfwafGiNigmy7aC8n2ye/f+u2zrCoROi8cP8XzJXGeFo/bHJkTAIDXvJg5w6/qnRD2LKY6kfZdUs+51e5m95kzV5/5XntphyBztpnaj+O+3crbe4tkfeKsEGfpyvVZa5Xdjxe+bXhpjcumbF7n22e7f9+zv4Kz4UfHT/58SWXO0nFbrG5iYZkTAICyd/RzRvp2HbaaT63LDXat29lk5kzWp5RJovG6x5mh6jdIc/XZpJllRv1vtI6nOqoz3/P8iWTmPGqfxP6t3D7ZMd5zvc4fP4XzJZVdS8dtYRX6OQEAeME7Mme04JI58+P6wk7UedTgiVpe6rNJj61Nvf9MJjk3pvGV4bR9e3az8vUJKxLOeKU+V9o5HEcdZstwn6b7ztJlXR+yOmYydtg++X7suu2zOwJTGfPU8VM+X0rZ9fzYdZkTAIBXvSdz5npz4p6ephuDHpv59zPP9XZezULpGu3qE9RmmB9auMSf8KdYmnX12+IfUQDabu+Jqp4NPpn6RMMymyb/a0pnuvUutPOuHYLygwqd6ku80A7hkz/jp4Am2qcr7d+K7RM+t3SI/m77a8dP/nzJlpM9To62TOYEAOAFbx9bywUVngFS/bmkNbz5WSgkeD4nAACvOZU5gw6Z3YzvCzDfo8KP21b5zZ7aqvzILxfEw6Rn2UsDAACsjjMn3y8YTSm7AQAAbyRzAgAAUIvMCQAAQC0yJwAAALXInAAAANQicwIAAFCLzAkAAEAttz6fc3okR9tPc+5/at/V8ovLz9tV4QEi86NJzrbArdt1g/vqsz6kZfN0x2XGT46f2u1w13pfW/79z7082I8Xz5Ivvz5cXq/zvXC+LzOd74fvTDTdXeLyr57vnscLwA+cypzJ/2MS04eumf5DeuUW9YSr5ZeW79vKj6rMtVt60du26w431WfomuX+pm/XGUPXLH/0bXAH89va4a71ppcPHpqausu8cPwURS38Sj3Xci618JdfH15ZufM9eb4vc9uua8KJv60d7lpvavnwI87CWT+OfVsxc8blv3S+33VdAuB/c3/mbPvpjqPCf0xXyy8sP3RN3f/aL9+D3rNdt7ipPn0bf3SebPDw1ua3tcNd6y0fh20fN9Q65933dsXtGuK8cKawL74+vLRy53v2fJ+ut0Mic/6idrhrvdn2CVNcLlu+O3NePd9lTgBec2vmXPpUhq6JPyRdP+Rtmib+P2/7oW84gGd+HXQNpcufl9x/VptYPuhgikpP1Oc56qiZCu/21VkX3zZR7p479SF3frvSMstn67PMaLquXbfghfpcaefNIsmjaHsf9vN2mLb1ucviqu7bp7x/67fPc/NTfQpnj5/C+ZIb81k6TrLtf0Pm/P3XB+d7nfP92TbbY+j/Ot/ns3xKfdvsN9f+Of1Z982hN70ls9+fLdEN2/M+Vf5h+8ucANzr3sx5XML0ier0Oiwg+l9vGqQ1xKMwD9eR+L//oE6baen6zJVZRo6tAaHv+/W923Xv2ye7vXfJ1Gd9PbXSPOOV+lxt53zpm5uqOwUf20d7Idk++f1bv32WVSVC54Xjp3i+JM7T4nGbc0fmPF7y89cH53vojvN9bZmrx9BJX3K+Z77sGg5+D79vue3wXFN84Tjsg48Ynsvlyj/eMpkTgBu9mDmX/7DPCXsWUx8q7z+ifs6t9r/bPnPm6jP/37u0Q3AP2mZqP477ditv7y2S9YnvHeIsXbk+a62y+/HCtw0vrXHZlM3rfPts9+979ldwNvzo+MmfL6nMWTpui9W9EFC/9frgfL+hVlFLbMaV/Lfne9zPuU7dHjbrZy5tPw0BmPpFU/3t21ruh5Pkyz9R30QTyJwAvOYd/ZyRvl2Hreb/81tuAGr995bMnMn6lO5R9uOmNuW99p/9S3L12aSZZUbt+iyVOrh1q/D9peQ96FH7JPZv5fbJjvGe63X++CmcL6ksVDpuC6uo1M8Z+fj1wfn+w0oVDqh6/Zxfcr4ntj6fCfu26Ya+nYbD9nPTlY/D+zKnfk4A7vWOzBktuNxT5sf1hZ0k8yi6E7W89Ml06r/edH3O3KOcG9P4yvC6aKxUUb4+YUXCGa/U5+pYsvC7Q6l9erbv7Hw7jPl70GT75Pu16rbP7ghMZZZTx0/5fCllofNjU3P3oPn98q3XB+f7Zi0/Pt+j0s6fwn/sfN/lw7XbM86Qaxv2bdu2TTfMQ2LX0cOJ7ZrfkvoSdab8oy2TOQG40XsyZ643J+7pabox6MFo+3X+8Yqu3hula7SrT1Cb6eXyCw1tH/2UQ7Ouflv8I7oh2m7viaqevRHK1CcaNtY0+V9TOnMvcqGdd+0QlB9U6NQN0IV2WFYb7qHtjtn+ikhy/1Zsn/CrXUP0d9tfO37y50u2nOxxcrRl+8UK++Urrw/O93SD/PR8D2efOSr+2vmeHLqbOiEfbbt+5XJYP1GJvqZ67jhM7/aw/KMKy5wA3OjtY2u5oPhVyJeLrDG8ra4K7cBFqQOntF9cH65zvk+c75+XO3Cc1wC85lTmTH88PUQ/isj9hldGe5VV+c2e2iq0A5ekh+SV94vrw1XO94nz/dPy5/vZnmoA2DjOnHy/YHCVezn445zvAMDvInMCAABQi8wJAABALTInAAAAtcicAAAA1CJzAgAAUIvMCQAAQC23Pp9z+on+tp/m3P8Ur+vlrw8N2D9tbJp55lkCtbfrrvXeVc+51bYlfKo+d5VfXH4+zis8YCLXntfrmT5kPTcPAIBf7FTmTN7LJqYPXTPd+J5Oc9dcLH/omiVq9u32DUPXtF3XnKll7e26a72v1TPzBPYb9u8vOx6Ky/dt5R2bO4/Si+a3K1fRC+UDAMAb3Z85235KeBVugC+W37dxF1HY1Tl0TdONw4XMWXG77lrvrfW8Yf/+suOhtPx0PNR0NXPmtit3zMqcAAD8TrdmzrFvp1vkTdfZOsJ1cwe9zGi6rl0HNYbDHKMkkCl/fUs+QcYB9Fn3k5kzud6p7k0zV7W4vc9RktPCbffcvvktt21vYvlnXeJaP6a8/Xhs6xmv4ez+vdRu4ziO47ruplnDXrIdwoGjw6bhbmifMdpd8Z7c16e8H7PHeaY9X9jvMicAAN/m3syZ0ffLTXJ4E72+jr8aGhZ8Nt2UM2dcylrzs5kzI+iGilojub3TSN9hHfG7DJK8f3s3tYx78OL0nZtUMcOEJU9NOL3OtsPcdGsH4Jl1nG2ftU6baen65Pdj7jgP1hC15yv7XeYEAODbvJg5w69KHos7ktYMFtw7x/f0Gz/7ot1ugOKmX+tHv7WzVG3zOr+9S3s+33H79u5MK56+I5j+PuB7M2fcQKnO3n07POdWq9E+c+bqk9uPY2a/R2sYjss/tv9ucqp8AAD4Her3c+7GMSYz5zrj1m/WHXSM/byfc585j7Y3kTkrf5Owb5tu6Nu279um61Ob/O7MGa16Hbaab4cloNWqUjJzJutT+uwgtd+j8obN36/VVD8nAABf5C2ZM/5uXjC2di0gnHF6mGG8ll1PUThucz/E9Pmu/Tf+LnU4JTNncnvz/WN3bW+hnm3bLr+DmthrP8+cV9otKnjJnPl2CDvV51G2R+4ZW5v5Pd/Dzw5SH3Wkxta+0qMtcwIA8F3e8X3O8KdVmjAKBMMLmyb/qy5nuoMSGWP70zCP7dcal8lHzz8sb1XbR6+T2zv/2kz40MUgpt2yveWFn1uZ3mlR5ty124m1XHoYSjyoNL/Lmm4MjpH5d1y3uyy/yWerlP0NoX19htJ+zBznhfa8vt9lTgAAvs1bfkPojOrPRzxZiXc+C+XXSPVzXnv//9lun+D5nAAAfJdTmTPdtxT/2OwPzU+H+KjzPx76t/w0rvyv7fZ+8XDxWfYUBQCAzzvOnDUFYwtllneLnib56coAAAB/02czJwAAAH+ZzAkAAEAtMicAAAC1yJwAAADUInMCAABQi8wJAABALTInAAAAtcicAAAA1CJzAgAAUIvMCQAAQC0yJwAAALXInAAAANQicwIAAFCLzAkAAEAtMicAAAC1yJwAAADUInMCAABQi8wJAABALTInAAAAtcicAAAA1CJzAgAAUMtB5nzk7Zcs/PnakofvvavYex02TmHimQJfaLEPtgYAAPA/O86cJ6ffnjnD6cvr35w595UsVzucW470t6R0sRMAAHi/e/o5kxH0fI9oruSXU9n+LeXNvEvYqZjckP3CybmHmfP8domaAADAB93Qz7nPTrm4tc+B5bh1PryVi31P7ioE4331TjZCedMKbZtTswEAAAC2bujnTIaiXJoaM9F0PzdXn/LEZLKKp/RttB1tP47jOHbNPKFp2nliPL0LpzTdMI7jsMye5ybbJ1n5RyZY7jdhU0KhbcuNAwAA8Ga3BZKjpHd2yeTccu4tvz03ceiaJVn27TNDjs8YGQTReaGha+LYGbw7CJzjLhOeyZxX638+ScqcAADAB5V+kueMZeH923PTC/144Rv3ixWqun99GORymTNaYg6Ty2LTW7qm7Zc/5syZDJmFnsmTVd03xWHblnfNZvncXAAAgJ87O5C1HF3Ov3ETw5ILnFnpvsxcNsuVE2bOMTWGduiabRDt22nK9NZ5/trPmVtXLnNuAnYuNOaCa2Fd5WU2JQMAAFRykDk3EagQivZhclPOmI9eYyYfJldRyJaFt4/juAmHm8wZTp9i52HmnDtC137Ocg33m1Ouf7lxwqL2b8n9CQAA8E7X+jkPO9nCubkFNoUUIms5ax2+3k08lTnHbaRcRWNr5ylN1yX7OcutV14yU//n62SsLZQgdgIAAJ9yrZ9zzH+ZsFDCfmJu4bFu5hy7Zv4O5tSb+ezFjL/OGUTN/G8IhV8F3f5obbImhU1YNjwXJl/b2NyKwuniKAAAUNWpfs5k7NwvvFn+sNjDufsKJFPZlRi2Pigl6OTs26Zp1oKjEbXrG+JnpTzCx6XE3aEnk/YmYxe24urG5ppI5gQAAN7s4AdmlliyiUabPHOYQjfTSxW63qGay6In13ivfYuN+cg3vpQ5c6my0AKjhAkAAHzC2RByPmom31iesi+wkLL24aqc1t4WtPbrKoTPcmgsZ87k65M1vLQ8AADADwkhAAAA1CJzAgAAUIvMCQAAQC0yJwAAALXInAAAANQicwIAAFCLzAkAAEAtJzLn0DWPx6Pt1yl9+3g8Hm0/zWm64eZKvVT+tHBi8d9f/+Ly83Y9NhtxWz3Pt8Ct23WD++ozt8Tj0XSJt/zw+KndDnet97Xl37lF4Xqz+3F3lmQvDZfLv8fva7cC19UX6+m66rr6k+V/0/UhfV0t+/Lrw+X1Ot8L5/sy0/l++M5E090lLv+++6hL14fjzDl0zXYdQ9dMK37lUnTCS+Un6pmb/tvqX1q+bytUcLPys2fFndt1h5vqM3TNch727Tpj6Jrlj74NzrTf1g53rTe9/Pr/SPJqeOH4KYpa+JV6ruUkt7Ticf6Su9rt2ipdV59cV6vXx3W1uPwfua4WCvvm68MrK3e+J8/3ZW7bdU048be1w13rTS0ffsRZOOvHsW8rZs64/Fvvo85fH17PnG0/HVkVbpxeKv/qvdEvqn9h+aFr6h6Cl6+V92zXLW6qT9/GH/EkGzw8pX5bO9y13vJx2PZxQ61zPpKdcts1xP+vhdNrHecv+W3tVniT6+oLXFddV4+X//7raqGwL74+vLRy53v2fJ+ut0Mic/6idrhrvdn2CVNZLlu+O3PedR91/vrwUuZcPjsbumZqxinFN03zeDwebffM9MlP9J5FPbtzu3Y3J1V+WJvHPpPn6nm6/rP1w4imaeJ9s/1wYp7YdMPyOtjgS/VPLx80W1R6oj7X27/UPsntLW9XWmb5bH2WGfOBsf+M6GR9rrTzZpETn+Lc0Q7Ttj53WVzVffuU92/99nlufuqzrLPHT+F8mbd3W07pOMm2/w2ZM1f+lfOueH3L1ie4/iz78dkK08LLH+n65Op52G6uq8n6uK66rtZsnz9wXc35yuuD873O+f5sm+0x9H+d7/NZPqW+bfaba/+c/qz75tCb3pLZ78+W6IbteZ8q/7D9f1vmzCy3XAfmVpuvff1SmXDjumazv0/U+Z57o2zZy5JT8p/ruRYQVXPe5PUDgx/Uv1CnzbR0fa63f7CGqH2y23uX/PHwfD1Eh8Yr9bnazvnSNyf/nYKPl6K9kGyf/P6t3z7LqhI3RxeOn+L5kjhPi8dtzh2ZM+3qeVe+vqXabf0vZCltHLetvmxfrp3vPR5cV11XV66rZf/fdbWw/FdeH5zvoTvO97Vlrh5DJ33J+d4Hnz/Hk6PDf5677fBcU3zhOOyDjxiey+XKP96yz2TO6NbncNH5o6LlI/nN522bDyI27fXDAzLZFhfqv61o6sOP/Ucpz7k/vJvN298b5epzvf2DNQzH5d8oWZ9498f3fJXrs9Yqux/DE/fONS6bsnmdb5/t/n3P/grOhh8dP/nzJXVvVDpui9VNLPzTzHn9vCtf3xLtFlc6/KR4XXaZWqjPrceD66rr6o9r5bp6UM/vva4mffH1wfl+Q62iltiMK/lvz/e4n3Oduj1s1s9c2n765HrqF13qUzgO98NJ8uWfqO+l+6iz14db+zmz+3KtSXhIHN10XfPzz+MjfbsOr8rvpOVArXVzlLw3StbnevsH5b12UL4kV5/N7l9m1K7PUqmD86XCOPvktfKofRL7t3L7ZMcizvU6f/wUzpfU/9ml47awikufz5119bw7ur5dypxLoetChfrcejy4rrqu/qhSrqv5av6B62ph+a+8Pjjff1ipwgH1w1v8nO853xNbn8+Efdt0Q99Ow2H7uenKx+F9mfPqfdT5ffuWzLn/0uo4jmuCX/48Ueeh4hiwaMHl2pcffxJ+mBeOi3ut/tnlE2PAMtt/sf2DNWzH+F2+MkR9+kWl4yHs2AnHhFyvz5V2DsfzhPdA4T49+xnO+XYY89fKZPuUsk3N9tkdgan/W08dP+XzpfR/9olb1/B9F66VV/bXtfPu6PqWardwwvZefP9jI7l2vvd4cF11XY3W4rpa8L9dV/P75VuvD873zVp+fL5HpZ0/hf/Y+b7Lh2u3Z5whw+/UtG3bdM/flQ1HDye2a37L7nTIl3+0Zb87cy6fOwUPe1kOm+DzvPnbu8993LRt+K3dH1077rg3yn3qGH8i2XTxFq/zTzfU6XM4XaNdfV5p/23xj+jE3W7viaqePWFzx8O6FdOMwvYeutDOu3aIcsKVlV5qh2W14R7a7pjtt92T+7di+6wFL9fnpabXjp/8+ZItJ3ucHG3Z+WvlpeP26nmXub7l222IrkDbaiVuz3P7/c7jwXU1t39dV89WZ1e+6+pa6DdfVwv75SuvD873dIP89HwPZ585Kv7a+b79vz0+UMLVtu36lcth/UQlPJlOHofp3R6Wf1Th3505X3JvwT+/N+JVfekrOy8XeflDp4+r0A5clDtwUteBuvvr5gtP5pc/a3Nd/RzX1Ynr6uelDpzSfnF9uM75PnG+f96V+6jS8nvHmfOZ0CscuLdnzvTHKNXqz9PwyqiEsr7Gb0vUVqEduCQ9dCR3aai8v+69vtX5Kswx19WPcV2duK5+Wv66mt8vrg9XOd8nzvdPu3YfdWXI7ngqc9ZxdXQN/4FgEIBrDt/stutbNGTm++4f+AVcV+H/4Xzn9/pY5gQAAODPkzkBAACoReYEAACgFpkTAACAWmROAAAAapE5AQAAqEXmBAAAoBaZEwAAgFpkTgAAAGqROQEAAKhF5gQAAKAWmRMAAIBaZE4AAABqkTkBAACoReYEAACgFpkTAACAWmROAAAAapE5AQAAqEXmBAAAoBaZEwAAgFpkTgAAAGo5zpyPxyP5+v32a0/W53wlz7/9hUb4bFt90A93CgAA8JdczpyhmhVLVKDwIrf8XnLJSxPLG16u2/8g+dHA4b4AAAD+pOx9fzngvTkwTKsL/y1Uo5D6cnF0P2UTii4F7309/57zSf58sAcAAP6e4y673L9vU0iDhdyYrOeZ2LyPTIUyC03xplbq26luTTe8Y3UpudCenJjblQAAwJ90NnNuJqbM6eep7W+r5HO9m4gSJs9kDcsJJ7kh5SBaborz+Wromu2MpjvXDGlD13w8c97yJwAA8Me8+BtChagwdM3NcTPTy3omc+4n7gtcppwJqOXFktVITunbMJT37bdlzsKRcDWKv6nGAADAJ1zInIUIF9pkzrVLr2maIFkF0+eJU0dp08xznsUkQ2YyhZ6v8GG2PGyEQug6M3HOnFPa7Nume/Z+Pje/7Z4N1PbPtmq6bulI3gbMZOZMtPDcFz0tvPzxLOKxXz4//fRHAIfNsl9GEAUAgD/j4OY+THcnO6nCzBlmob5dQ0vXrLFp6Jo4dj7nbHJUIcUl8+SZOufCarkdNguX42VudX2QH9e3Dc+GWdpk6Q7tmsc6XHnomnjo8j5zllo4eOuyr8ICwuJz05Pbntwdy6z93PKSAADAH3D5+5zhrFk0NDTu5wy/5BlEpji69O0cbMJENL8+DI1hwAsXOBNvwtfJFeXKPJk5c0vG/ZyzuWWWDBlmzrDNNk24zZyFFg5j5DI18QXTuR7J6XGjbbY02ar7BQRLAAD4HxwPgzwR4QqZM1pqHcZ5JXMeVmOTXi5lzk1qSpYTrign98ZcfeKNm5UyZylUXsqcS6HrQkPXJL9QmpsebM6+9QpNPR61OQAA8Mfc8xtCay/cEA7qjJPSkjm3CSqIrJnMmexpTP65r2QyHB4mnzMx8kxYzU3fZM7nn8V+zkf0bdjDsbWZFl7eHsXQbT/q4fQxfzDs/7zUzymRAgDAX3Itc+YnrmNo0z8gtJkTD9p8zljKaPvodbDSM71qm0iTzMwn35tcslBgcsp+VmLIavCrQY+2f/6cUDdMzdD2Y9c0bRv+ms/SL7wtpk+tY5sb+3Y3LS5q97NDm+mXWnXMfGpwuCQAAPDt7sqc1S1RZJ92kpnntcx5OPF8HD1T7Hk3Pwwl7uS8qrwtmwbZZ8j9Ai/XBAAA+OUuZ85kiqgqF1qS4XNfvU0/4L6c5Br3bz9cvjD35811b+as8AjVcTw3YlnCBACA/4oA8AVST9p8STRO9taOUwAAgBSZEwAAgFpkTgAAAGqROQEAAKhF5gQAAKAWmRMAAIBaZE4AAABq+ROZc34EiKd/jOM4rE9Wedz8FM7vb+f1YTGbp84sM6o8uBQAAP5fX5Q5g4dLph5TOXTNLVmob01YivMAAAY8SURBVL84U41j31aOTXe18/sNXbMcOX27xsuha5Y/vnzvAwDAr/NFmXMcx3Ecuqbt+zaRC743C91p6JpUIL97DV/ZzvFh07fJhqqe2QEA4P+Sz5xTt2LTNPtBh1GP4zCO84DO58Jt9xzeGXUkpbson7PO3uY/E0EqGCSyUHK988SmG5bXaydXcuzofnuPrCtumma36rP1Ga+1T1DNuJT9esv7q7i9Z9v5XEXjopYZTde1a43uO37CVSV3pcgJAAD3KvZz9m0YKtd79L5fbsu7Zr5Hn0YuDusIxuX2PXzrsL5h+ft8ZliKTESDfRbKrneuajSqMl9OenvzwhKmJny9PlczVaqfM73e/P4qb++Fds7JlB8fS+tG33f8hO/avuWZdyVOAAC41VHmXG7BN6/33WlD17T9GGSS5zvCTqptB9xFQR3mta22Wai83ufcc2N0k9t7UNHd4q/W55p95sytN7e/jrb3WjsnFY+ftdR9J+cPj5917dl29n1OAAC41/XMGfcRrYsUMudN3zDMjh2d17/NQvn1LkHmeOxobnvPV3oZzvpSfa5JZs7kekufEZS291I7p9dbPH62M279hmqqY3sj8z1PAADgJS9lzvi7iAeZszwe9fzYyF32SGWfzZjPdLnh75fOo1rz5eS2Ny8qYMmcr9XnnrG1qfef+Yzg3NjjM8Np0zWMyw8LDmfcc/zE45zDbBm2efh7tgAAwM8d/YbQdD8fP70w/AmYza/QPNr++fM03dC3qXc84p+BOZkZwucqDtHfbb/t/nxEQXm73mGt6eZHg7LlJLa3WN14MOim8+58fa60T6L08E279Q6l/ZXZ3mvtfK6ecXsGw2ibpinVf3GhfXb1D8oJVqyTEwAA7vRtz0rhP+EHZAEA4E+QOfmN/JYPAAD8DTInv0cw/FUnJwAA/AkyJwAAALXInAAAANQicwIAAFCLzAkAAEAtMicAAAC1yJwAAADU8scyZ98+Ho+m+9ry32t+NMn3PglzfbhKYqf0rWeuAADAp31N5hy65rGRzn59WzcT1i7/3Yau+dLMOXTNcgz07TZeDl3Tdl0jcwIAwEd9TeYcx7Fvw1iRy34y5zXfmzn7Nqx4vF+Grmm6cZA5AQDgw/KZcxq22DRz92IU94K+xmEc517I58Jt9+yTnN8S9lFuAttz1qlkMGfOKV0EGSMov+2f0591nAt+/jm9ZV//cIu7YTvmNFV+2fqGpmlS9XzsJjbdsLxem+Nk+5TbP7O9y1u3Ewv7Kylb/jKj6bp2rdFNx8OmDuGqn9skcwIAwKcV+zn7NgyV6z193y838l0z54NppOOwjnhcuiXDtw7rG5a/L2TOVFDp2zXphN+33IbDNZUk6x+u4zlpWi5XflbYVlMTzuvKtMPcdFNrRPU53z759i9tbypzlvZXUqb8+NhYN+Ku4yF+V5TT16NW5gQAgI86ypzLLfvmdWDNFW0/Bnf8z3ckvoj5Yg6I+znXqVFkmgZVjuM4jlOFumZKfcEGJOufLK1YfrGmu+LL7fCc+4NRrrn2L2/vPnO+sL+Kx8Na6r6T82fHQ7D2aAvi6nzxLyQBAMAfcD1zxn1K6yKFzHnTFyDj73OuU3OZsG+bbujbaThsP0egXP2TpRXLP1vpafniG5cg9npAKmX+/PYmM+elDTw6HrYz7jsexlTH8Ha2fk4AAPiolzJn/F3Eg8yZGs+5euX7nJs/o8nhWNaxb9u2bbphHhK7jhJO1H9+yy715cvPiELckjnz7RD+/uo8yjacd659zmT+VERLja290vOYLz8sOJxx4/EQ7IvU92xlTgAA+LSj3xCa7v/D1/FPxmx+tebR9s+fs5l/iWf7jkec2U5njMSQzPV7l+FQ1nb9yuWwJrjo66j7+u8r+dgEzVT5GXFNNzF52w7D2nKpB2Ze+g2hTPuf3N5gLfn9lZIpP9pnTdNkGvTF4yFR/+3Xd5fJxtYCAMDHfNOzUvhi6YHRAADAHydz8g7hj/8CAAD/D5mTeoLhrzo5AQDgvyRzAgAAUIvMCQAAQC0yJwAAALXInAAAANQicwIAAFCLzAkAAEAtMicAAAC1yJwAAADUInMCAABQi8wJAABALTInAAAAtcicAAAA1CJzAgAAUMs/EaYZcp4ZpkEAAAAASUVORK5CYII=" alt="" />
五、与org.json比较
json-lib和org.json的使用几乎是相同的，我总结出的区别有两点：
1. org.json比json-lib要轻量得多，前者没有依赖任何其他jar包，而后者要依赖ezmorph和commons的lang、logging、beanutils、collections等组件
2. json-lib在构造bean和解析bean时比org.json要方便的多，json-lib可直接与bean互相转换，而org.json不能直接与bean相互转换而需要map作为中转，若将bean转为json数据，首先需要先将bean转换为map再将map转为json，比较麻烦。
总之，还是那句话—适合自己的才是最好的，大家要按需选取使用哪种方法进行解析。最后给大家介绍两款解析Json数据的工具：一是在线工具JSONEdit（http://braincast.nl/samples/jsoneditor/）；另一个是Eclipse的插件JSON Tree Analyzer，都很好用，推荐给大家使用！