diff --git b/.idea/encodings.xml a/.idea/encodings.xml
new file mode 100644
index 0000000..f13fa33
--- /dev/null
+++ a/.idea/encodings.xml
@@ -0,0 +1,4 @@
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new file mode 100644
index 0000000..f6a3f5c
--- /dev/null
+++ a/.idea/greedy.iml
@@ -0,0 +1,12 @@
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\ No newline at end of file
diff --git b/.idea/misc.xml a/.idea/misc.xml
new file mode 100644
index 0000000..5417b68
--- /dev/null
+++ a/.idea/misc.xml
@@ -0,0 +1,4 @@
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diff --git b/.idea/modules.xml a/.idea/modules.xml
new file mode 100644
index 0000000..a2538dd
--- /dev/null
+++ a/.idea/modules.xml
@@ -0,0 +1,8 @@
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\ No newline at end of file
diff --git b/.idea/workspace.xml a/.idea/workspace.xml
new file mode 100644
index 0000000..068b5e6
--- /dev/null
+++ a/.idea/workspace.xml
@@ -0,0 +1,249 @@
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diff --git b/Readme.txt a/Readme.txt
new file mode 100644
index 0000000..49258d5
--- /dev/null
+++ a/Readme.txt
@@ -0,0 +1,3 @@
+project2_main.py python执行脚本（所有的代码位置）
+data_train.xlsx 意图分类训练数据
+stopword.txt 停用词
diff --git b/data_train.xlsx a/data_train.xlsx
new file mode 100644
index 0000000..86f353b
Binary files /dev/null and a/data_train.xlsx differ
diff --git b/description.pdf a/description.pdf
new file mode 100644
index 0000000..7387216
Binary files /dev/null and a/description.pdf differ
diff --git b/main.py a/main.py
new file mode 100644
index 0000000..0d6bc9d
--- /dev/null
+++ a/main.py
@@ -0,0 +1,243 @@
+# coding=utf-8
+
+import pandas as pd
+import fool
+import re
+import random
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+
+
+# -----------------------------------------------------
+# 加载停用词词典
+stopwords = {}
+with open(r'stopword.txt', 'r', encoding='utf-8') as fr:
+ for word in fr:
+ stopwords[word.strip()] = 0
+# -----------------------------------------------------
+
+
+# 定义类
+class CLF_MODEL:
+ # 类目标：该类将所有模型训练、预测、数据预处理、意图识别的函数包括其中
+
+ # 初始化模块
+ def __init__(self):
+ self.model = "" # 成员变量，用于存储模型
+ self.vectorizer = "" # 成员变量，用于存储tfidf统计值
+
+ # 训练模块
+ def train(self):
+ # 函数目标：读取训练数据，训练意图分类模型，并将训练好的分类模型赋值给成员变量self.model
+ # input：无
+ # output：无
+
+ # 从excel文件读取训练样本
+ d_train = pd.read_excel("data_train.xlsx")
+ # 对训练数据进行预处理
+ d_train.sentence_train = d_train.sentence_train.apply(self.fun_clean)
+ print("训练样本 = %d" % len(d_train))
+
+ """
+ TODO：利用sklearn中的函数进行训练，将句子转化为特征features
+ """
+
+
+ self.model.fit(features, d_train.label)
+
+ # 预测模块（使用模型预测）
+ def predict_model(self, sentence):
+ # 函数目标：使用意图分类模型预测意图
+ # input：sentence（用户输入）
+ # output：clf_result（意图类别），score（意图分数）
+
+ # --------------
+ # 对样本中没有的特殊情况做特别判断
+ if sentence in ["好的", "需要", "是的", "要的", "好", "要", "是"]:
+ return 1, 0.8
+ # --------------
+
+ """
+ TODO：利用已训练好的意图分类模型进行意图识别
+ """
+
+
+ return clf_result, score
+
+ # 预测模块（使用规则）
+ def predict_rule(self, sentence):
+ # 函数目标：如果模型训练出现异常，可以使用规则进行预测，同时也可以让学员融合"模型"及"规则"的预测方式
+ # input：sentence（用户输入）
+ # output：clf_result（意图类别），score（意图分数）
+
+ sentence = sentence.replace(' ', '')
+ if re.findall(r'不需要|不要|停止|终止|退出|不买|不定|不订', sentence):
+ return 2, 0.8
+ elif re.findall(r'订|定|预定|买|购', sentence) or sentence in ["好的","需要","是的","要的","好","要","是"]:
+ return 1, 0.8
+ else:
+ return 0, 0.8
+
+ # 预处理函数
+ def fun_clean(self, sentence):
+ # 函数目标：预处理函数，将必要的实体转换成统一符号（利于分类准确），去除停用词等
+ # input：sentence（用户输入语句）
+ # output：sentence（预处理结果）
+
+ """
+ TODO：预处理函数，将必要的实体转换成统一符号（利于分类准确），去除停用词等
+ """
+
+
+ return sentence
+
+ # 分类主函数
+ def fun_clf(self, sentence):
+ # 函数目标：意图识别主函数
+ # input：sentence（ 用户输入语句）
+ # output：clf_result（意图类别），score（意图分数）
+
+ # 对用户输入进行预处理
+ sentence = self.fun_clean(sentence)
+ # 得到意图分类结果（0为“查询”类别，1为“订票”类别，2为“终止服务”类别）
+ clf_result, score = self.predict_model(sentence) # 使用训练的模型进行意图预测
+ # clf_result, score = self.predict_rule(sentence) # 使用规则进行意图预测（可与用模型进行意图识别的方法二选一）
+ return clf_result, score
+
+
+def fun_replace_num(sentence):
+ # 函数目标：替换时间中的数字（目的是便于实体识别包fool对实体的识别）
+ # input：sentence
+ # output：sentence
+
+ # 定义要替换的数字
+ time_num = {"一":"1","二":"2","三":"3","四":"4","五":"5","六":"6","七":"7","八":"8","九":"9","十":"10","十一":"11","十二":"12"}
+ for k, v in time_num.items():
+ sentence = sentence.replace(k, v)
+ return sentence
+
+
+def slot_fill(sentence, key=None):
+ # 函数目标：填槽函数（该函数从sentence中寻找需要的内容，完成填槽工作）
+ # input：sentence（用户输入）, key（指定槽位，只对该句话提取指定槽位的信息）
+ # output：slot（返回填槽的结果，以json格式返回，key为槽位名，value为值）
+
+ slot = {}
+ # 进行实体识别
+ words, ners = fool.analysis(sentence)
+
+ """
+ TODO：从sentence中寻找需要的内容，完成填槽工作
+ """
+
+
+ return slot
+
+
+def fun_wait(clf_obj):
+ # 函数目标：等待，获取用户输入问句
+ # input：CLF_MODEL类实例化对象
+ # output：clf_result（用户输入意图类别）, score（意图识别分数）, sentence（用户输入）
+
+ # 等待用户输入
+ print("\n\n\n")
+ print("-------------------------------------------------------------")
+ print("----*------*-----*-----*----*-----*-----*-----*-----*------")
+ print("Starting ...")
+ sentence = input("客服：请问需要什么服务？(时间请用12小时制表示）\n")
+ # 对用户输入进行意图识别
+ clf_result, score = clf_obj.fun_clf(sentence)
+ return clf_result, score, sentence
+
+
+def fun_search(clf_result, sentence):
+ # 函数目标：为用户查询余票
+ # input：clf_result（意图分类结果）, sentence（用户输入问句）
+ # output：是否有票
+
+ # 定义槽存储空间
+ name = {"time":"出发时间", "date":"出发日期", "from_city":"出发城市", "to_city":"到达城市"}
+ slot = {"time":"", "date":"", "from_city":"", "to_city":""}
+ # 使用用户第一句话进行填槽
+ sentence = fun_replace_num(sentence)
+ slot_init = slot_fill(sentence)
+ for key in slot_init.keys():
+ slot[key] = slot_init[key]
+ # 对未填充对槽位，向用户提问，进行针对性填槽
+ while "" in slot.values():
+ for key in slot.keys():
+ if slot[key]=="":
+ sentence = input("客服：请问%s是？\n"%(name[key]))
+ sentence = fun_replace_num(sentence)
+ slot_cur = slot_fill(sentence, key)
+ for key in slot_cur.keys():
+ if slot[key]=="":
+ slot[key] = slot_cur[key]
+
+ # 查询是否有票，并答复用户（本次查询是否有票使用随机数完成，实际情况可查询数据库返回）
+ if random.random()>0.5:
+ print("客服：%s%s从%s到%s的票充足"%(slot["date"], slot["time"], slot["from_city"], slot["to_city"]))
+ # 返回1表示有票
+ return 1
+ else:
+ print("客服：%s%s从%s到%s无票" % (slot["date"], slot["time"], slot["from_city"], slot["to_city"]))
+ print("End !!!")
+ print("----*------*-----*-----*----*-----*-----*-----*-----*------")
+ print("-------------------------------------------------------------")
+ # 返回0表示无票
+ return 0
+
+
+def fun_book():
+ # 函数目标：执行下单订票动作
+ # input：无
+ # output：无
+
+ print("客服：已为您完成订票。\n\n\n")
+ print("End !!!")
+ print("----*------*-----*-----*----*-----*-----*-----*-----*------")
+ print("-------------------------------------------------------------")
+
+
+
+if __name__=="__main__":
+ # 实例化对象
+ clf_obj = CLF_MODEL()
+ # 完成意图识别模型的训练
+ clf_obj.train()
+ # 用户定义阈值（当分类器分类的分数大于阈值才采纳本次意图分类结果，目的是排除分数过低的意图分类结果）
+ threshold = 0.55
+ # 循环提供服务
+ while 1:
+ clf_result, score, sentence = fun_wait(clf_obj)
+ # -------------------------------------------------------------------------------
+ # 状态转移条件（等待-->等待）：用户输入未达到“查询”、“订票”类别的阈值 OR 意图被分类为“终止服务”
+ # -------------------------------------------------------------------------------
+ if score<threshold or clf_result==2:
+ continue
+
+ # -------------------------------------------------------------------------------
+ # 状态转移条件（等待-->查询）：用户输入分类为“查询” OR “订票”
+ # -------------------------------------------------------------------------------
+ else:
+ # 收集订票细节信息
+ search_result = fun_search(clf_result, sentence)
+ # 查询无票
+ # -------------------------------------------------------------------------------
+ # 状态转移条件（查询-->等待）：FUN_SEARCH执行完后用户输入意图为“终止服务” OR FUN_SEARCH返回无票
+ # -------------------------------------------------------------------------------
+ if search_result==0:
+ continue
+ # 查询有票
+ else:
+ # 等待用户输入
+ sentence = input("客服：需要为您订票吗？\n")
+ # 对用户输入进行意图识别
+ clf_result, score = clf_obj.fun_clf(sentence)
+ # -------------------------------------------------------------------------------
+ # 状态转移条件（查询-->订票）：FUN_SEARCH返回有票 AND 用户输入意图为“订票”
+ # -------------------------------------------------------------------------------
+ if clf_result == 1:
+ fun_book()
+ continue
+
diff --git b/stopword.txt a/stopword.txt
new file mode 100644
index 0000000..a2664c4
--- /dev/null
+++ a/stopword.txt
@@ -0,0 +1,13 @@
+的
+到
+一下
+我
+，
+帮
+去
+了
+？
+吧
+。
+请
+么