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20260609.1.py

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+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.metrics import classification_report, accuracy_score
+
+
+genre_map = {
+    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"
+}
+
+
+df = pd.read_csv("movie_data.csv")  
+X = df["text"]
+y = df["label"]
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.2, random_state=42, stratify=y
+)
+
+
+tfidf = TfidfVectorizer(max_features=5000, ngram_range=(1, 2))
+X_train_tfidf = tfidf.fit_transform(X_train)
+X_test_tfidf = tfidf.transform(X_test)
+
+model = MultinomialNB()
+model.fit(X_train_tfidf, y_train)
+
+
+y_pred = model.predict(X_test_tfidf)
+print(f"准确率: {accuracy_score(y_test, y_pred):.4f}")
+print(classification_report(y_test, y_pred, target_names=genre_map.values()))
+
+
+def predict_genre(text):
+    text_tfidf = tfidf.transform([text])
+    pred_label = model.predict(text_tfidf)[0]
+    return genre_map[pred_label]
+
+new_movie = "一群年轻人在宇宙飞船上探索外星文明，遭遇未知危险"
+print(f"电影简介：{new_movie}")
+print(f"预测类别：{predict_genre(new_movie)}")