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1.py

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+import warnings
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+warnings.filterwarnings("ignore", category=UserWarning)
+
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.svm import LinearSVC
+from sklearn.metrics import accuracy_score, classification_report
+
+genre_dict = {
+    0: "剧情",
+    1: "喜剧",
+    2: "科幻",
+    3: "悬疑",
+    4: "动作",
+    5: "爱情",
+    6: "动画",
+    7: "犯罪",
+    8: "奇幻",
+    9: "纪录"
+}
+num_classes = len(genre_dict)
+
+def load_data(file_path="movie_data.csv"):
+    df = pd.read_csv(file_path)
+    texts = df["text"].astype(str).tolist()
+    labels = df["label"].astype(int).tolist()
+    return texts, labels
+
+def text_feature_extraction(texts):
+    vectorizer = TfidfVectorizer(
+        max_features=10000,
+        stop_words="english",
+        ngram_range=(1, 2)
+    )
+    features = vectorizer.fit_transform(texts)
+    return features, vectorizer
+
+def train_and_evaluate(features, labels):
+    X_train, X_test, y_train, y_test = train_test_split(
+        features, labels, test_size=0.2, random_state=42, stratify=labels
+    )
+    model = LinearSVC(random_state=42, max_iter=10000)
+    model.fit(X_train, y_train)
+    y_pred = model.predict(X_test)
+    acc = accuracy_score(y_test, y_pred)
+    print(f"测试集准确率: {acc:.4f}")
+    print("\n分类报告:")
+    print(classification_report(y_test, y_pred, target_names=genre_dict.values()))
+    return model
+
+def predict_genre(model, vectorizer, new_text):
+    new_feature = vectorizer.transform([new_text])
+    pred_label = model.predict(new_feature)[0]
+    return genre_dict[pred_label]
+
+if __name__ == "__main__":
+    texts, labels = load_data()
+    features, vectorizer = text_feature_extraction(texts)
+    model = train_and_evaluate(features, labels)
+    sample_text = "一个孤独的科学家发明了时间机器，却在穿梭时空的过程中陷入了悖论..."
+    print(f"\n示例文本: {sample_text}")
+    print(f"预测类型: {predict_genre(model, vectorizer, sample_text)}")