public class Test
        public int aa{get;set;}
也是不能对应的。
有复杂层次的 json，可以使用 “类中类” 来映射，要注意 List<T>/Array/ArrayList的类型的使用。
 Json 转为 Model

新建一个 Json 文件，名字随意，例如 json1.json
把以下内容粘贴进去
  "refresh_token": "25.ea2f85ebd48df85fe5400000.18640.282335-15533349",
  "expires_in": 2592010,
  "session_key": "9mzdWr3n8ncMeDgX8zjhkhlW8khb5cdZtPevPbPwQGBg==",
  "access_token": "24.ac0ca9fakhlkyhl552017858.282335-15533349",
  "scope": "audio_voice_assistant_get audio_tts_post public vis-ocr_ocr nlp_simnet nlp_wclassify_watermark brain_ocr_scope vis-classify_car brain_gif_antiporn brain_ocr_general brain_ocr_general_basic brain_ocr_generer vis-classify_animal brain_politician brain_unit_utterance brain_imgquality_general brain_nlp_simnet brain_nlp_depparser vis-classify_plant brain_solution brain_ocr_plate_number brain_nlp_wordembedding brain_nlp_dnnlm_cn_legacy brain_nlp_simnet_legacy brain_nlp_commain_animal_classify brain_plant_classify brain_solution_iocr brain_realtime_product brain_nlp_lexer_custom brain_kgbody_analysis brain_body_attr brain_ocr_vat_invoice brain_advanced_general_classify brain_numbers brain_body_number vis-faceverify_FACE_auth_sessionkey smartapp_swanid_verify smartapp_opensource_openapi",
  "session_secret": "2ca66d464545c77a4767f709873be4"
定义一个模型，文件名为 AccessTokenModel.cs
    public class AccessTokenModel
        public string refresh_token { get; set; }
        public string expires_in { get; set; }//： Access Token的有效期(秒为单位，一般为1个月)
        public string scope { get; set; }
        public string session_key { get; set; }
        public string access_token { get; set; }//： 要获取的Access Token
        public string session_secret { get; set; }
打开 Program.cs 文件
            public static void Main(string[] args)
                FileStream fs = new FileStream(@"请修改成你的文件路径\json1.json", FileMode.Open);
                StreamReader fileStream = new StreamReader(fs);
                string str = "";
                string line;
                while ((line = fileStream.ReadLine()) != null)
                    str += line;
                }
　　　　　　　　　　//上面的代码没有意义，只是将Json文件的内容加载到字符串中
　　　　　　　　　　
                JObject jObject = new JObject();　　　　　　　　//新建 操作对象
                AccessTokenModel a = JsonConvert.DeserializeObject<AccessTokenModel>(str);
                Console.WriteLine(a.access_token);　　　　//随意输出一个属性
                Console.ReadKey();
JsonConvert.DeserializeObject<要转化的模型类>("字符串对象");

之后可以很方便的把Json文件的内容存放到数据库中。
把Json文件改成以下的样子
  "refresh_token": "25.ea2f85ebd48df85fe5400000.18640.282335-15533349",
  "expires_in": 2592010,
  "session_key": "9mzdWr3n8ncMeDgX8zjhkhlW8khb5cdZtPevPbPwQGBg==",
  "access_token": "24.ac0ca9fakhlkyhl552017858.282335-15533349",
  "scope": "audio_voice_assistant_get audio_tts_post public vis-ocr_ocr nlp_simnet nlp_wclassify_watermark brain_ocr_scope vis-classify_car brain_gif_antiporn brain_ocr_general brain_ocr_general_basic brain_ocr_generer vis-classify_animal brain_politician brain_unit_utterance brain_imgquality_general brain_nlp_simnet brain_nlp_depparser vis-classify_plant brain_solution brain_ocr_plate_number brain_nlp_wordembedding brain_nlp_dnnlm_cn_legacy brain_nlp_simnet_legacy brain_nlp_commain_animal_classify brain_plant_classify brain_solution_iocr brain_realtime_product brain_nlp_lexer_custom brain_kgbody_analysis brain_body_attr brain_ocr_vat_invoice brain_advanced_general_classify brain_numbers brain_body_number vis-faceverify_FACE_auth_sessionkey smartapp_swanid_verify smartapp_opensource_openapi",
  "session_secret": "2ca66d464545c77a4767f709873be4"
  "refresh_token": "25.ea2f85ebd48df85fe5400000.18640.282335-15533349",
  "expires_in": 2592010,
  "session_key": "9mzdWr3n8ncMeDgX8zjhkhlW8khb5cdZtPevPbPwQGBg==",
  "access_token": "24.ac0ca9fakhlkyhl552017858.282335-15533349",
  "scope": "audio_voice_assistant_get audio_tts_post public vis-ocr_ocr nlp_simnet nlp_wclassify_watermark brain_ocr_scope vis-classify_car brain_gif_antiporn brain_ocr_general brain_ocr_general_basic brain_ocr_generer vis-classify_animal brain_politician brain_unit_utterance brain_imgquality_general brain_nlp_simnet brain_nlp_depparser vis-classify_plant brain_solution brain_ocr_plate_number brain_nlp_wordembedding brain_nlp_dnnlm_cn_legacy brain_nlp_simnet_legacy brain_nlp_commain_animal_classify brain_plant_classify brain_solution_iocr brain_realtime_product brain_nlp_lexer_custom brain_kgbody_analysis brain_body_attr brain_ocr_vat_invoice brain_advanced_general_classify brain_numbers brain_body_number vis-faceverify_FACE_auth_sessionkey smartapp_swanid_verify smartapp_opensource_openapi",
  "session_secret": "2ca66d464545c77a4767f709873be4"
            public static void Main(string[] args)
                FileStream fs = new FileStream(@"请修改成你的文件路径\json1.json", FileMode.Open);
                StreamReader fileStream = new StreamReader(fs);
                string str = "";
                string line;
                while ((line = fileStream.ReadLine()) != null)
                    str += line;
                //上面的代码没有意义，只是将Json文件的内容加载到字符串中
                JObject jObject = new JObject();　　　　　　　　//新建 操作对象
                List<AccessTokenModel> a = JsonConvert.DeserializeObject<List<AccessTokenModel>>(str);
                foreach (var i in a)
                    Console.WriteLine(i.access_token);
                Console.ReadKey();
将Model转为Json

能够将模型对象转为 Json。
继续使用上面的 AccessTokenModel.cs 文件，
            public static void Main(string[] args)
                AccessTokenModel accessTokenModel = new AccessTokenModel();
                accessTokenModel.access_token = "test1";
                accessTokenModel.expires_in = "test2";
                accessTokenModel.refresh_token = "test3";
                accessTokenModel.scope = "test4";
                accessTokenModel.session_key = "test5";
                accessTokenModel.session_secret = "test6";
                JObject jObject = new JObject();
                string str = JsonConvert.SerializeObject(accessTokenModel);　　　　//转为字符串
                Console.WriteLine(str);
                Console.ReadKey();
JsonConvert.SerializeObject(a模型对象)；


运行后可以看到控制台输出的是Json字符串了，你可以继续把他放到Json文件中，这里不再赘述。
将 LINQ 转为 JSON

下面这个是从官网直接copy的例子，Jarray 是其框架提供的一种类型。
在控制台运行后会发现输出的字符是已经格式化的。
            public static void Main(string[] args)
                JArray array = new JArray();
                array.Add("Manual text");
                array.Add(new DateTime(2000, 5, 23));
                JObject o = new JObject();
                o["MyArray"] = array;
                string json = o.ToString();
                //   "MyArray": [
                //     "Manual text",
                //     "2000-05-23T00:00:00"
                Console.WriteLine(json);
                Console.ReadKey();
Linq 操作

框架提供了对 Jobject 对象的Linq操作支持
using Newtonsoft.Json.Linq;
之后你可以像操作数组、集合或者Context一样方便。
命名空间、类型、方法大全

本来想翻译一下的，英语太差，算了。在常用的类型前面加粗吧
Classes




    

 ClassDescription
DefaultJsonNameTable
The default JSON name table implementation.
笔者在弄了一段时间的百度 Ai 平台的SDK，封装了OCR SDK，由于现在在找实习工作，所以有部分没有弄完，有兴趣可以添加笔者的微信免费获取。微信在右侧导航栏。
百度AI 识别文字，返回Json结果, 名字随意.格式建议为 json，如果使用记事本保存，注意编码格式是 utf-8，因为c# string默认为utf8，不然会乱码。
  "log_id": 3413661945235258919,
  "direction": 0,
  "words_result_num": 2,
  "words_result": [
      "vertexes_location": [
          "y": 81,
          "x": 51
          "y": 81,
          "x": 151
          "y": 103,
          "x": 151
          "y": 103,
          "x": 51
      "probability": {
        "variance": 0.0,
        "average": 0.999861,
        "min": 0.999627
      "chars": [
          "char": "今",
          "location": {
            "width": 17,
            "top": 83,
            "left": 60,
            "height": 20
          "char": "天",
          "location": {
            "width": 17,
            "top": 83,
            "left": 78,
            "height": 20
          "char": "除",
          "location": {
            "width": 12,
            "top": 83,
            "left": 103,
            "height": 20
          "char": "了",
          "location": {
            "width": 16,
            "top": 83,
            "left": 116,
            "height": 20
          "char": "皮",
          "location": {
            "width": 13,
            "top": 83,
            "left": 140,
            "height": 20
      "min_finegrained_vertexes_location": [
          "y": 81,
          "x": 51
          "y": 81,
          "x": 151
          "y": 103,
          "x": 151
          "y": 103,
          "x": 51
      "finegrained_vertexes_location": [
          "y": 81,
          "x": 51
          "y": 81,
          "x": 71
          "y": 81,
          "x": 90
          "y": 81,
          "x": 110
          "y": 81,
          "x": 129
          "y": 81,
          "x": 149
          "y": 81,
          "x": 151
          "y": 91,
          "x": 151
          "y": 100,
          "x": 151
          "y": 103,
          "x": 151
          "y": 103,
          "x": 132
          "y": 103,
          "x": 112
          "y": 103,
          "x": 93
          "y": 103,
          "x": 73
          "y": 103,
          "x": 54
          "y": 103,
          "x": 51
          "y": 93,
          "x": 51
          "y": 84,
          "x": 51
      "location": {
        "width": 102,
        "top": 81,
        "left": 51,
        "height": 24
      "words": "今天除了皮"
      "vertexes_location": [
          "y": 109,
          "x": 52
          "y": 109,
          "x": 152
          "y": 130,
          "x": 152
          "y": 130,
          "x": 52
      "probability": {
        "variance": 8E-05,
        "average": 0.9907,
        "min": 0.973259
      "chars": [
          "char": "又",
          "location": {
            "width": 16,
            "top": 111,
            "left": 61,
            "height": 20
          "char": "啥",
          "location": {
            "width": 12,
            "top": 111,
            "left": 85,
            "height": 20
          "char": "也",
          "location": {
            "width": 16,
            "top": 111,
            "left": 98,
            "height": 20
          "char": "没",
          "location": {
            "width": 15,
            "top": 111,
            "left": 123,
            "height": 20
          "char": "干",
          "location": {
            "width": 13,
            "top": 111,
            "left": 141,
            "height": 20
      "min_finegrained_vertexes_location": [
          "y": 109,
          "x": 52
          "y": 109,
          "x": 152
          "y": 130,
          "x": 152
          "y": 130,
          "x": 52
      "finegrained_vertexes_location": [
          "y": 109,
          "x": 52
          "y": 109,
          "x": 71
          "y": 109,
          "x": 91
          "y": 109,
          "x": 110
          "y": 109,
          "x": 129
          "y": 109,
          "x": 149
          "y": 109,
          "x": 152
          "y": 119,
          "x": 152
          "y": 129,
          "x": 152
          "y": 130,
          "x": 152
          "y": 130,
          "x": 133
          "y": 130,
          "x": 113
          "y": 130,
          "x": 94
          "y": 130,
          "x": 74
          "y": 130,
          "x": 55
          "y": 130,
          "x": 52
          "y": 121,
          "x": 52
          "y": 111,
          "x": 52
      "location": {
        "width": 102,
        "top": 109,
        "left": 52,
        "height": 22
      "words": "又啥也没干"
  "language": -1
对应的模型 ，将 cs 文件，名字  GeneralModel.cs
    /// <summary>
    /// 通用文字识别（含位置版）返回结果
    /// </summary>
    public class GeneralModel
        /// <summary>
        /// 必选
        /// 唯一的log id，用于问题定位
        /// </summary>
        public long log_id { get; set; }
        /// <summary>
        /// 图像方向，当detect_direction=true时存在。
        /// 非必选
        ///- -1:未定义，
        ///- 0:正向，
        ///- 1: 逆时针90度，
        ///- 2:逆时针180度，
        ///- 3:逆时针270度
        /// </summary>
        public int direction { get; set; }
        /// <summary>
        /// 必选
        /// 识别结果数，表示words_result的元素个数
        /// </summary>
        public int words_result_num { get; set; }
        /// <summary>
        /// 检测语言 默认值会返回 -1
        /// </summary>
        public string language { get; set; }
        /// <summary>
        /// 定位和识别文字结果数组
        /// </summary>
        public List<Words_result> words_result { get; set; }
        public class Words_result
            /// <summary>
            /// 图片中文字段四个顶点位置(矩形范围)
            /// </summary>
            public List<XY> vertexes_Location { get; set; }
            /// <summary>
            /// 可选
            /// 行置信度信息；如果输入参数 probability = true 则输出
            /// </summary>
            public Probability probability { get; set; }
            /// <summary>
            /// 每个字
            /// </summary>
            public List<Chars> chars { get; set; }
            /// <summary>
            /// 最小细粒度顶点坐标
            /// </summary>
            public List<XY> min_finegrained_vertexes_location { get; set; }
            /// <summary>
            /// 细粒度顶点坐标，多边形
            /// </summary>
            public List<XY> finegrained_vertexes_location { get; set; }
            /// <summary>
            /// 文字在图片中的相对位置
            /// </summary>
            public Location location { get; set; }
            /// <summary>
            /// 识别出的文字
            /// </summary>
            public string words { get; set; }
            /// <summary>
            /// 坐标
            /// </summary>
            public class XY
                public int x { get; set; }
                public int y { get; set; }
            /// <summary>
            /// 行置信度
            /// </summary>
            public class Probability
                /// <summary>
                /// 行置信度平均值方差
                /// </summary>
                public double variance { get; set; }
                /// <summary>
                /// 行置信度平均值
                /// </summary>
                public double average { get; set; }
                /// <summary>
                /// 行置信度最小值
                /// </summary>
                public double min { get; set; }
            /// <summary>
            /// 单个文字
            /// </summary>
            public class Chars
                /// <summary>
                /// 识别的单个文字
                /// </summary>
                public char chaR { get; set; }
                /// <summary>
                /// 该文字范围(矩形)
                /// </summary>
                public Location location { get; set; }
        public class Location
            public int left { get; set; }
            public int top { get; set; }
            public int width { get; set; }
            public int height { get; set; }
可用控制台进行检验
 static void Main(string[] args)
            StreamReader streamReader = new StreamReader(System.IO.File.OpenRead(@"json文件位置"));
            string str = "";
            string jsonstr;
            while ((jsonstr = streamReader.ReadLine()) != null)
                str += jsonstr;
            GeneralModel generalModel = JsonConvert.DeserializeObject<GeneralModel>(str);
            Console.WriteLine("图片id：" + generalModel.log_id);
            Console.WriteLine("图像方向：" + generalModel.direction);
            Console.WriteLine("检测语言为：" + generalModel.language);
            Console.WriteLine("有几个结果：" + generalModel.words_result_num);
            foreach (var item in generalModel.words_result)
                Console.WriteLine("识别结果：" + Encoding.UTF8.GetString(Encoding.UTF8.GetBytes(item.words)));
                foreach (var itemi in item.vertexes_Location)
                    Console.WriteLine("{x:" + itemi.x + ";y:" + itemi.y + "}");
                Console.WriteLine("Probability:可信度:" + "行置信度平均值" + item.probability.average + ";行置信度平均值方差:" + item.probability.variance + ";行置信度平均值最小值：" + item.probability.min);
                foreach (var itemi in item.chars)
                    Console.WriteLine(itemi.chaR);
                    Console.WriteLine("位置:  left:" + itemi.location.left + ";  height: " + itemi.location.height + "top: " + itemi.location.top + "; width: " + itemi.location.width);
            Console.ReadKey();