diff --git a/model/trainModel.py b/model/trainModel.py
index e69de29..683a4b1 100644
--- a/model/trainModel.py
+++ b/model/trainModel.py
@@ -0,0 +1,45 @@
+import pandas as pd  # 用于数据处理
+import numpy as np  # 用于科学计算
+import csv  # 用于读取CSV文件
+from snownlp import SnowNLP  # 用于中文自然语言处理（此处未实际使用）
+from sklearn.feature_extraction.text import TfidfVectorizer  # 用于文本特征提取
+from sklearn.naive_bayes import MultinomialNB  # 用于多项式朴素贝叶斯分类
+from sklearn.model_selection import train_test_split  # 用于划分训练集和测试集
+from sklearn.metrics import accuracy_score  # 用于计算模型准确度
+
+def getSentiment_data():
+    # 从CSV文件中读取情感数据
+    sentiment_data = []
+    with open('./target.csv', 'r', encoding='utf8') as readerFile:
+        reader = csv.reader(readerFile)
+        for data in reader:
+            sentiment_data.append(data)
+    return sentiment_data
+
+def model_train():
+    # 获取情感数据并转换为DataFrame
+    sentiment_data = getSentiment_data()
+    df = pd.DataFrame(sentiment_data, columns=['text', 'sentiment'])
+
+    # 将数据集划分为训练集和测试集，测试集占20%
+    train_data, test_data = train_test_split(df, test_size=0.2, random_state=42)
+
+    # 初始化TfidfVectorizer，并对训练集和测试集进行文本特征提取
+    vectorize = TfidfVectorizer()
+    X_train = vectorize.fit_transform(train_data['text'])
+    y_train = train_data['sentiment']
+    X_test = vectorize.transform(test_data['text'])
+    y_test = test_data['sentiment']
+
+    # 初始化多项式朴素贝叶斯分类器，并进行训练
+    classifier = MultinomialNB()
+    classifier.fit(X_train, y_train)
+
+    # 对测试集进行预测
+    y_pred = classifier.predict(X_test)
+
+    # 计算模型准确度
+    accuracy = accuracy_score(y_test, y_pred)
+
+if __name__ == "__main__":
+    model_train()  # 训练模型并计算准确度